Guidelines to the edge: Integrating information and communication technologies in education
Bibliographic record
Abstract
Many institutions are in a state of flux in relation to the implementation and use of information communication technology (ICT) by faculty, staff, and students. Earlier research (Stockley 2002) indicates that most institutions are neither at the beginning – nor the end of the implementation continuum; rather, they are more likely to be found somewhere in the middle as institutions vary in their degree of integration. Institutions that are characterized by technological innovation are a rarity: most of us would rather read about bleeding edge technologies than experience the unwanted fallout first hand; nor can most institutions make the necessary financial commitments associated with being on the leading edge. This paper briefly visits historical aspects of technology innovations, implementation strategies and finally, focuses on approaches or guidelines to assist in getting to the edge.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".