Publication activity
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
Research across participating journals · All insights follow your collection filters.
Publication activity alongside incoming citations.
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Leading affiliations and themes in this collection.
Full distributions & methodology ↓Independent sources. Clear time scales.
Crossref and OpenAlex · separate totals for the same publication cohorts
Hover over a year or focus the chart and use arrow keys to inspect values.
Latest lifetime totals, grouped by the cited article’s year. These are not citations received in that year.
OpenAlex · incoming citation links grouped by citing-work year
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.
Articles by publication year
Latest lifetime totals · grouped by publication year
Latest lifetime totals · grouped by publication year
By year of the citing work · 2017–2026
2026 is year to date. Crossref and OpenAlex lifetime charts share a citation scale. Unknown observations appear as gaps; known zero stays zero. Annual receipts use available OpenAlex histories and may not sum to lifetime totals. 0 undated publications excluded from publication-year charts.
Crossref citation coverage: 2 / 2 publications (100.0%). Latest observation: 21 Oct 2026, 07:22 UTC. Means and percentages use records with known counts.
DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.
Observation range: 21 Oct 2026 00:22 UTC — 21 Oct 2026 07:22 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.
OpenAlex affiliations and primary-topic classification. Each publication counts once per institution or country; multi-affiliated publications appear in multiple groups, so percentages can sum above 100%. Countries describe author affiliations, not nationality. Topics use one primary topic per publication.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.
Citations are the latest recorded totals for articles published in each year, not citations received during that year. Crossref and OpenAlex remain separate and share the same citation scale. 2026 is incomplete; newer articles have had less time to receive citations.
0 undated publications excluded. Unknown citation counts are shown as —, never as zero. Partial coverage can understate totals; expand each table for known / total records.
| Publication year | Journal / scope | Publications | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2022 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 2 / 2 selected publications. Supported display window: 2017–2026; 2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Missing histories and years beyond a saved observation’s window remain unknown (—); partial coverage can understate totals.
Annual-history observations (UTC): 2026-10-21T00:22:03.841735+00:00 — 2026-10-21T07:22:47.394903+00:00. Older observations may be retained after failed or incomplete refreshes.
| Citation year | Journal / scope | OpenAlex · Citations received by year | Publications | Annual history known |
|---|---|---|---|---|
| 2017 | All selected publications | 0 | 2 | 2/2 |
| 2018 | All selected publications | 0 | 2 | 2/2 |
| 2019 | All selected publications | 0 | 2 | 2/2 |
| 2020 | All selected publications | 0 | 2 | 2/2 |
| 2021 | All selected publications | 0 | 2 | 2/2 |
| 2022 | All selected publications | 0 | 2 | 2/2 |
| 2023 | All selected publications | 0 | 2 | 2/2 |
| 2024 | All selected publications | 1 | 2 | 2/2 |
| 2025 | All selected publications | 0 | 2 | 2/2 |
| 2026 | All selected publications | 0 | 2 | 2/2 |
Counts represent citation links, not unique citing papers across a journal: one paper citing two selected articles contributes two links. Older citations outside this window are excluded, so annual sums need not equal lifetime totals. OpenAlex methodology ↗
Publications by publication year. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2022 | 1 |
| 2023 | 1 |
Latest Crossref totals for the selected publications; not citations received in the selected years.
Unknown counts are excluded from the mean, median and cited-publication rate.
Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.
| Journal / publisher | Publications | Crossref citations | OpenAlex citations | OpenAlex coverage | Crossref mean | Crossref median | Crossref cited %* | Crossref coverage |
|---|---|---|---|---|---|---|---|---|
| International Journal of Finance & Banking Studies SSBFNET | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 1 | 0 | 1 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
* Percentage among publications with a known citation count.
Does agricultural activity affect human mortality? This research paper provides an empirical analysis on this important question by separately regressing agricultural outputs and its components on human mortality rate in Nigeria over the period 1981–2019. Employing the autoregressive distributed lag estimator, a long run relationship exist between agricultural outputs and human mortality rate in Nigeria. Regarding the parameter estimates, findings showed that the indirect relationship between agriculture outputs and mortality is statistically insignificant in the long run. More so, in the short run, the current level of agriculture outputs reported a significant positive coefficient whereas the first lag has a significant negative parameter. Concerning the agriculture components, in the long run, fishing activity negatively and significantly influenced human mortality whereas forestry activity directly impacted on human mortality rates. The influence of both crop and livestock productions on mortality rate is not statistically established in the long run. The study therefore suggests the need for government to adopt pro-poor policies that is capable of improving investments in agricultural activities by both public and private sectors so as to curtail human mortality rate in the country.
This study examines the relationship between government expenditure on health and economic growth in Botswana. It seeks to test the existence of cointegration and specification of the deterministic components with special reference to the Pantula Principle. This helps to overcome the shortfall of the method by Johansen, which may lead to spurious results by omitting the presence of deterministic components in the analysis. The cointegration approach is used and tested using three methods by Engle and Granger (1987) or EG, a procedure suggested by Johansen (1988) and error correction model (ECM) approach proposed by Granger(1988) and short-run analysis is made using the pairwise granger causality tests. Findings show that the correct model specification for testing long-run relationships consists of one cointegrating vector with a constant which is the most restrictive hypothesis according to the Pantula principle. Using the Johansen approach, total health expenditure and recurring health expenditure have a cointegration relationship with growth while development health expenditure and growth are not cointegrated. The ECM and the approach by EG confirm a weak and/or no cointegration between the variables. Growth has no effect on government expenditure on health in the short run, but a cointegration relationship suggests that it may marginally contribute to an increase in health expenditure over the long term. The study clarifies the correct model to test for cointegration and specification for the deterministic component. It confirms the existence of a healthcare expenditure-led growth hypothesis. This requires Botswana to design a policy that targets specific parts of recurrent and development health expenditure to support human capital development and influence future growth.
This study examines the relationship between government expenditure on health and economic growth in Botswana. It seeks to test the existence of cointegration and specification of the deterministic components with special reference to the Pantula Principle. This helps to overcome the shortfall of the method by Johansen, which may lead to spurious results by omitting the presence of deterministic components in the analysis. The cointegration approach is used and tested using three methods by Engle and Granger (1987) or EG, a procedure suggested by Johansen (1988) and error correction model (ECM) approach proposed by Granger(1988) and short-run analysis is made using the pairwise granger causality tests. Findings show that the correct model specification for testing long-run relationships consists of one cointegrating vector with a constant which is the most restrictive hypothesis according to the Pantula principle. Using the Johansen approach, total health expenditure and recurring health expenditure have a cointegration relationship with growth while development health expenditure and growth are not cointegrated. The ECM and the approach by EG confirm a weak and/or no cointegration between the variables. Growth has no effect on government expenditure on health in the short run, but a cointegration relationship suggests that it may marginally contribute to an increase in health expenditure over the long term. The study clarifies the correct model to test for cointegration and specification for the deterministic component. It confirms the existence of a healthcare expenditure-led growth hypothesis. This requires Botswana to design a policy that targets specific parts of recurrent and development health expenditure to support human capital development and influence future growth.
Does agricultural activity affect human mortality? This research paper provides an empirical analysis on this important question by separately regressing agricultural outputs and its components on human mortality rate in Nigeria over the period 1981–2019. Employing the autoregressive distributed lag estimator, a long run relationship exist between agricultural outputs and human mortality rate in Nigeria. Regarding the parameter estimates, findings showed that the indirect relationship between agriculture outputs and mortality is statistically insignificant in the long run. More so, in the short run, the current level of agriculture outputs reported a significant positive coefficient whereas the first lag has a significant negative parameter. Concerning the agriculture components, in the long run, fishing activity negatively and significantly influenced human mortality whereas forestry activity directly impacted on human mortality rates. The influence of both crop and livestock productions on mortality rate is not statistically established in the long run. The study therefore suggests the need for government to adopt pro-poor policies that is capable of improving investments in agricultural activities by both public and private sectors so as to curtail human mortality rate in the country.
Source coverage refers to this result set; a publication may occur in several sources. Sync times refer to contributing journals.
Missing abstracts: 0 · Missing authors: 0 · Missing publication dates: 0 · Dates without day precision: 0
| Source | Most recent successful journal sync | Latest attempt |
|---|---|---|
| crossref | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |
| ojs-rest | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 06:46 UTC |
| oai-pmh | 20 Oct 2026 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |