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 universités commencent à peine à ressentir l’impact de l’augmentation de l’utilisation des technologies en ligne sur les politiques académiques et les procédures conçues à l’origine pour le contexte traditionnel de l’enseignement en face à face. Cette étude de cas, en relatant l’expérience d’une université, tente de démontrer quels types de politiques doivent être examinés, modifiés ou créés. Des exemples de politiques et de problématiques communes à la plupart des universités sont examinés. Ils comprennent les tâches et responsabilités des formateurs, l’évaluation des cours, la notation et l’évaluation des étudiants, les archives et la vie privée, la libération des droits d’auteur du matériel de tierces parties et la propriété intellectuelle. Les études suggèrent que le travail de mise à jour des politiques dans un environnement en évolution peut être envisagé comme un défi, mais il est important de lui donner une direction, tant au niveau micro- que macro-politique. Ce travail consistant à prendre en compte les problématiques politiques, même au niveau micro, peut comprendre des tâches relativement simples comme clarifier le sens des mots jusqu’à des tâches complexes comme la négociation 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 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 | 0.043 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.030 | 0.040 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".