Bibliographic record
Abstract
Popularized by Elfreda A. Chatman, the theory of a small world, which refers to community of like-minded individuals who share coownership of social reality, tends to be viewed in a negative light despite its possible benefits. This article examines the potential for the development of small worlds within the context of professional degrees. This potential is explored primarily through the author’s personal experiences obtaining two professional degrees: a Bachelor of Education at Tyndale University College & Seminary and a Master of Library and Information Science at the University of Western Ontario. Each of the four core concepts of Chatman’s small world theory – worldview, social norms, social types, and information behaviour – is investigated in detail through the lens of the author’s experiences. The possible advantages and disadvantages for students should a small world develop are then discussed.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".