Bibliographic record
Abstract
Universities are just beginning to experience the impact of the increasing use of online technologies on academic policies and procedures originally formulated for the traditional face-to-face teaching context. In this case study, the experience of one university is used to demonstrate the types of policies that require examination and modification as well as the areas in which new policies may be required. Examples of policies and issues that are common to most universities are examined, and include instructor responsibilities and workload, course evaluation, grading and evaluation of students, privacy and records, copyright clearance of third-party materials, and ownership of intellectual property. The review suggests that the work involved in policy updating in a changing environment can be challenging but it is important that direction be provided at both the micro and macro policy levels. The work involved in addressing policy issues, even at the micro level, can range from the relatively simple tasks of providing clearer wording to changes requiring collective bargaining. Les universites commencent a peine a ressentir l’impact de l’augmentation de l’utilisation des technologies en ligne sur les politiques academiques et les procedures concues a l’origine pour le contexte traditionnel de l’enseignement en face a face. Cette etude de cas, en relatant l’experience d’une universite, tente de demontrer quels types de politiques doivent etre examines, modifies ou crees. Des exemples de politiques et de problematiques communes a la plupart des universites sont examines. Ils comprennent les tâches et responsabilites des formateurs, l’evaluation des cours, la notation et l’evaluation des etudiants, les archives et la vie privee, la liberation des droits d’auteur du materiel de tierces parties et la propriete intellectuelle. Les etudes suggerent que le travail de mise a jour des politiques dans un environnement en evolution peut etre envisage comme un defi, mais il est important de lui donner une direction, tant au niveau micro- que macro-politique. Ce travail consistant a prendre en compte les problematiques politiques, meme au niveau micro, peut comprendre des tâches relativement simples comme clarifier le sens des mots jusqu’a des tâches complexes comme la negociation de conventions collectives.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".