Faculty Liaisons: an embedded approach for enriching teaching and learning in higher education
Bibliographic record
Abstract
This paper explores the experiences of a group of academic developers who support educational development work as Faculty Liaisons at a large, research-intensive university. These academic developers inhabit complex ‘third spaces’, providing support through an embedded partnership relationship that requires lateral movement across functional and organizational boundaries to create new professional spaces, knowledge, and relationships. The authors utilize narrative inquiry and auto-ethnographic approaches to present an interpretive qualitative analysis of their experiences supporting Faculty and University projects across complex and evolving organizational boundaries. From this analysis, they highlight key roles and responsibilities associated with their blended context and identify challenges that academic developers who occupy third spaces within academic organizations face as they negotiate competing interests, identities, and requirements associated with the diverse range of their projects and the blended experience of working in scholarly and administrative, central- and Faculty-based roles. The lessons they have learned from these experiences will be of particular interest to academic developers who are experiencing the flux of change within higher education settings that are impacting teaching and learning practices both for faculty in the classroom and for those across the institution who support them.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| 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".