Making just tenure and promotion decisions using the objective knowledge growth framework
Bibliographic record
Abstract
Purpose – The purpose of this paper is to utilize the Objective Knowledge Growth Framework (OKGF) to promote a better understanding of the evaluating tenure and promotion processes. Design/methodology/approach – A scenario is created to illustrate the concept of using OKGF. Findings – The framework aims to support decision makers in identifying the challenges they face, encourages them to act on their tentative theories, and to be attentive to the outcomes. It requires a deeper understanding of the problem by shifting the spotlight to the teaching scores; multiple perspectives for reaching a solution; testing different options (solutions/theories) by seeking out information that challenges ones beliefs and biases so as not to overlook a viable solution; and ensuring a fair and just process. Research limitations/implications – Research can be expanded by developing an empirical research study and to validate the findings. Practical implications – It is a high stakes evaluation that may result in unjustified dismissal. Originality/value – The paper is original in that the tenure process has not come under scrutiny under a conceptual framework such as the OKGF.
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 | 0.032 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".