A Knowledge Management Framework for Institutional Research
Bibliographic record
Abstract
Abstract This chapter presents a new conceptual framework of institutional research (IR). The framework refines previously studied dimensions of IR and integrates them into the higher order concept of knowledge management. Previously studied dimensions of IR include the institution’s organizational sectors (e.g., academic, human resources), the functions for which information is used (e.g., operations, strategic management), and the resources supporting IR (e.g., technology, funding). The framework innovates by specifying what competencies are required to carry out IR activities and how to assign a level of development to each competency. This operationalization permits the creation of an assessment tool enabling us to move from general and intuitive statements about development to specific and behavioral levels which are actionable. The framework formulation was validated with a group of IR experts in Chile. The framework can be used to assess one institution, to compare an institution to a peer group, or to compare groups of institutions at the regional, national, or international levels.
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How this classification was reachedexpand
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 | 0.025 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads 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".