Evaluating Agricultural Economists within Colleges and Faculties of Agriculture
Bibliographic record
Abstract
There is an increasing tendency for individual faculty evaluations, in whole or in part, to be done outside the department. Historically, this was not true. College committees acted only to uphold standards, ensure equity, and monitor procedural correctness within a department's evaluation process. Deans tended not to participate in routine faculty evaluations. Financial metrics like return on investment were not calculated on an individual faculty basis. Incentives from all administrative levels tended to be consistent with disciplinary success. These are no longer true for many agricultural economics departments. Both department and faculty responses have been mixed. At the department level, some have not responded while others have embraced a life science model. At the individual faculty level, some have not responded while others have chased money, impact factors, and publication counts. I explore the consequences of these changes for agricultural economists in a college dominated by life sciences with two caveats. First, this is an opinion article and represents my perceptions of general tendencies; there exist many exceptions in either direction. Second, I have biases which are undoubtedly reflected throughout. Il y a une tendance croissante aux évaluations individuelles des professeurs, en tout ou en partie. Ces évaluations sont réalisées de l'extérieur du département. Historiquement, ce n′était pas vrai. Les comités des collèges agissaient uniquement pour respecter les normes, assurer l′équité et surveiller l'exactitude procédurale au sein du processus d′évaluation d'un département. Les doyens universitaires ont tendance à ne pas participer aux évaluations routines de la faculté académique. Les statistiques financières, comme le rendement du capital investi, n'ont pas été calculées individuellement au niveau de la faculté. Les mesures incitatives de tous les niveaux administratifs ont tendance à être liées avec la réussite dans le domaine en question. Ceci n'est plus vrai pour de nombreux départements d’économie agricoles universitaires. Les réponses des départements et du corps professoral ont été mitigées. Au niveau départemental, certains n'ont pas répondu alors que d'autres ont adopté le modèle des sciences de la vie. Au niveau de la faculté individuelle, certains n'ont pas répondu alors que d'autres ont chassé de l'argent, des facteurs d'impact et une quantité de publication. J'explore les conséquences de ces changements pour les économistes agricoles dans un collège dominé par les sciences de la vie. Je souligne deux mises en garde. Premièrement, il s'agit d'un article d'opinion et représente ma perception des tendances générales; Il existe de nombreuses exceptions dans les deux directions. Deuxièmement, j'ai des préjugés qui seront reflétés tout au long.
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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 | 0.027 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".