Bibliographic record
Abstract
Are cooperatives gender sensitive?A confirmatory and predictive analysis of women's collective entrepreneurship MAriA BAstidA, AnA OlveirA and Miguel Ángel vÁzquez tAín Classifying the degree of cooperative multinationality: Case study of a French multinational cooperative Anjel errAsti, ignACiO BretOs and CArMen MArCuellO Manager bonding and the technical efficiency of cooperative credit unions-parametric and nonparametric analyses MiChAel Adusei, KwAsi POKu and sAMuel AKOMeA the socio-economic impact of certification schemes in conflict-affected regions: the case of arabica coffee in the eastern drC wAnnes slOsse, jerOen Buysse, KOen sChOOrs, ivAn gOdFrOid, MiChAelA BOyen and MArijKe d'hAese Classifying responsible investors: identifying clusters of Ontario investors AnthOny PisCitelli e-commerce adoption among Moroccan agricultural cooperatives: Between structural challenges and immense business performance potential iMAd jABBOuri, rAChid jABBOuri, KAriM BAhOuM and yAsMine el hAjjAji entrepreneurial ecosystem for cooperatives: the case of Kyrgyz agricultural cooperatives nAziK BeishenAly and FrédériC duFAys the resurrection of earlier imprints post mortem: explaining the turkish agricultural cooperative movement with an imprinting theory lens, 1888-1937 CeMil OzAn sOydeMir and MehMet erçeK iCt diffusion in public administrations and business dynamics: evidence from italian municipalities niCOlA MAtteuCCi, rAFFAellA sAntOlini and silviO di FABiO Productivity drivers of infrastructure companies: network industries utilizing economies of scale in the digital era ryOtA nAKAtAni does the mixed-ownership reform improve the productivity of state-owned enterprises?evidence from companies listed in Chinese stock FAn zhAng, Fei wAng and qiAO wAng the regulatory effect of cooperation degree in increasing tobacco farmers' income by mitigating production risk shocks ruOyAn zhAng and ru Chen
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.871 | 0.819 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".