Academic continuity planning in higher education
Bibliographic record
Abstract
Crisis planning in institutions of higher learning has focused largely on emergency management, physical safety and technology recovery. While these are clearly important, many aspects of planning for continuity have largely been ignored, including planning for the post-secondary education sector's core business, that is, the delivery of graduate and undergraduate academic programmes. Use of the term 'academic continuity' as a component of institutional planning is relatively new in higher education. Discussions on academic continuity have often centred on the use of technology-enhanced education and online teaching in the event of crisis or disaster affecting campuses. However, online approaches are only one aspect of academic continuity planning. Using examples of two different crisis situations encountered at a large urban university spanning three campuses, this paper presents an approach to academic continuity planning that can potentially serve as a model for other institutions.
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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".