Information and communication policy development in higher education: a case study of the vice-president's Task Force on Information Technology (TFIT) at Brock University
Bibliographic record
Abstract
Increasingly, university administrations across North America are looking at Information and Communication Technology (ICT) as a way to address a variety of institutional problems such as rapidly increasing student enrolments, declining or drastically limited human and financial resources, and increased global competition for higher education services. At the same time Information and Communication Technology is presented in the media, and perceived by the general public, as a means for universities to accommodate the access and learning needs of a growing, diverse and technologically sophisticated learner population. Realizing either of these views of ICT requires continuous research, planning and the development of strategic policy initiatives all of which have tended to lag behind the purchase and implementation of technology at many higher education institutions. This study represents an insider's view of the policy research and planning process for Information and Communication at Brock University in Ontario, Canada. The insider perspective was achieved through semi-structured interviews with a group of faculty and staff working as a Vice Presidential Task Force on Information Technology (TFIT) at the university. The researcher, was a participant-observer on the committee, and as such was well positioned to continent on the nature and meaning of the processes and outcomes of the group's work. This study examined and interpreted the process and implications of one higher education institution's ICT policy research and development efforts from the perspective of those individuals engaged in that process. Their experiences and informed perceptions, insights and recommendations offer a starting point for further examination of how the process of policy development for ICT in higher education can, or should be, managed in an increasingly complex higher education environment.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.042 | 0.012 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".