A Multi-Island Situation Without the Ocean: Tutors' perceptions about working in isolation from colleagues
Bibliographic record
Abstract
Distance education is generally seen as a very isolating experience for students, but one often forgets that it can be an equally isolating experience for teaching staff, who sometimes must work in isolation from colleagues. This study examines the experiences of nine tutors at one of the 10 biggest universities in the world, Universtiy of South Africa's (Unisa) Reading and Writing Centres. The tutors all work at different Regional Offices across South Africa. This study examines both quantitative data (closed-ended questions) and qualitative data (open-ended questions) obtained from questionnaires. This study seeks to determine to what extent administrative support, professional development support, and colleague support influence tutors' feelings of isolation. This paper takes the position that if feelings of isolation are curbed, staff retention will be improved, which in turn means that the university retains valuable experience. Findings show that contact with and collaboration between colleagues significantly decrease feelings of isolation. Other important methods of curbing isolation are regular training and continuous administrative support.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".