Bibliographic record
Abstract
Annales de l'économie publique, sociale et coopérative Mapping the relevance and influence of gender in social economy enterprises: A review based on bibliographic analysis EsthEr GArcíA-río, FrAncisco rincon-roldAn and PEdro BAEnA-lunA unveiling the role of government support: Empirical studies on the performance of cooperatives in Vietnam An thi thuy duonG Gender blindness in the 'next Generation European union' funds: Projects for Economic recovery and transformation social economy as an exception to the rule MAríA BAstidA, MiGuEl Á. VÁzquEz tAín, AlBErto VAquEro GArcíA and MAríA luisA dEl río ArAújo towards sustainable climate-smart agriculture: A cost-benefit analysis of a modernized irrigation district in spain nicolA coMincioli, cristinA El Khoury, dAVidE BAzzAnA, dEMis lEGrEnzi, FErnAndo nArdi, dAniEl A. sEGoViA-cArdozo, sErGio VErGAlli and lEonor rodríGuEz-sinoBAs What factors influence the vertical integration of agricultural cooperatives?-Evidencefrom 500 cooperatives in heilongjiang, china yuxin liu, lihAn cAo, runqi Guo and EryAnG liu the economic potential of home sharing cooperatives for active ageing of older people cArlos rosA-jiMénEz, ruBén MorA-EstEBAn, GErMÁn ortEGA-PAloMo and juAn MArcos cAstro-BonAño instant lottery innovation, tax compliance and herd effects, an impact assessment in Brazil jorGE luis tonEtto, AdElAr FochEzAtto, josEP MiquEl PiquE and cArinA rAPEtti unveiling the potential of community financing for entrepreneurs: A systematic review and research agenda sAMAnWitA MishrA and chAndAn KuMAr sAhoo
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.867 | 0.785 |
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".