Scientific Journal of Agricultural Economics
Bibliographic record
Abstract
Scientific Journal of Agricultural Economics ISSN 1923-6514 (Online): Library & Archives Canada 3(1), 2015 Founder and Executive DirectorGhada Gomaa A. Mohamed Editor-in-ChiefDaniel E. May Editorial BoardProfessor Caroline SaundersLincoln University, NZDr(c) Ane Ripoll ZagarraUniversity of Keele, UKDr Andrew WatsonHarper Adams University, UKDr Bahar Ali KazmiKeele University, UKDr Daniel E. MayHarper Adams University, UKDr Edward DickinHarper Adams University College, UKDr Elena DruicaUniversity of Bucharest, RomaniaDr (c) Jason YipBirkbeck College, London University, UKDr Jose Roberto ParraRBB Economics, UKDr Keith WalleyHarper Adams University College, UKDr Nicola RandallHarper Adams University College, UKDr(c) Ouarda DsouliUniversity of Northampton, UKDr Peter TaitLincoln University, NZDr Priscila HermidaOxford University, UKDr(c) Treasa KearneyKeele University, UK Editorial iR.V. Ramanamurthy and Emmadi Naveen KumarSmall Farmer Economy and Their Crisis in Rural India: A Study inThree Villages1Adam BaxterAn Investigation into the Causes of Residential Property PriceVariation in London’s Surrounding Area10Sara Arancibia, Alexander Abarca and Gonzalo Moya 33Facing down the ‘perfect storm’ – is it time to think the unthinkable? Areview of global warming science and policy https://epe.lac-bac.gc.ca/100/201/300/scientific_jrn_agricultural_economics/2015/SJAE(31).pdf
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.244 | 0.139 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".