Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Results obtained from our past research programs were used to provide managers with models and tools that concern the process of organizational knowledge management. The models and tools are based on actual data. They aim at allowing the alignment of the continuous training to the business strategic plan. The analysis of empirical data allowed the finding of factors, criteria and weights to be incorporated in a model named ECONOF. This paper first presents this multicriterion model with the software tool related. An example of criteria and subcriteria is presented. The process of finding criteria with their associate weights is explained. Results produced by ECONOF are then presented. The model helps managers to find continuous training activities that fit the priorities and resources set in the strategic plan. Then, we explain why we decided to move towards a mathematical model. The basic mathematical model that was developed from a subset of criteria and subcriteria used and validated with the existing ECONOF multicriterion model is then presented. Future enhancements of the mathematical model are finally mentioned.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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 it