Third space professionals and undergraduate teaching – A comparative study in China, the United Kingdom and Canada
Bibliographic record
Abstract
Abstract This research explores the emerging position of teaching professionals in research universities through the framework of ‘third space professionals’. The third space is described as an overlapping space between the professional and academic domains in university and usually staff who are neither traditional academics nor traditional professionals work in this space and considered as the third space professionals. This research is achieved through three case studies, each in China, the United Kingdom and Canada. By investigating the role and positioning of teaching professionals in higher education, this research provides empirical evidence supporting the existence of the third space for professionals in higher education. The findings show that the responsibilities of the teaching professionals are relatively similar at each university, mainly including training academics and supporting curriculum development. However, their positioning in the university and value in supporting teaching and learning can be influenced by their relationship with academics. Universities’ expectations of teaching professionals play an important role in shaping their collective identities. By proposing a three‐dimensional space model for higher education, this research builds on previous studies on the higher education space and offers an alternate framework for interpreting higher education space and analysing university staffing.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".