Bibliographic record
Abstract
Purpose This paper seeks to report on the successful collaboration in an academic learning commons environment, established collaboratively between the University of Guelph and Humber College, where library reference and writing center support services are integrated and therefore model student behaviour when writing academic papers. Design/methodology/approach After examining previous collaborative efforts between libraries and writing centres, this paper focuses on the relationship between thinking and the writing process itself as the theoretical framework on which a successful collaboration between the University of Guelph‐Humber Library Reference Services and Humber College's Writing Center exists. Statistical data relating to the increased usage levels of reference services, when made available in a writing centre environment, as well as library services satisfaction data are reported. Findings Researching and writing anxiety is diminished when students are able to access both reference (researching) services and writing support services in the same location. The increased exchange and interface between reference service providers and writing tutors, while assisting students to write better academic papers, reflects the fact that thinking, researching and writing are interwoven and recursive processes that are further enhanced when supported by their physical collocation. Academic institutions should model and further facilitate the collocation and integration of the research and writing processes by reorganizing services and service delivery units to better reflect student behaviour. Research limitations/implications When this paper was written, service level usage data that had been gathered fully supported the continued collaboration of the reference and writing centre services. However, further research needs to be conducted regarding the impact of this cross‐functional, student support service on student success. Originality/value This paper is of interest to academic leaders and service providers who are interested in furthering collaborations between students' service providers, primarily library reference staff and writing centre tutors, whose partnership naturally reflects the integrated and recursive research and writing processes.
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 | 0.009 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.025 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.341 | 0.309 |
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".