Digital Images in Teaching and Learning at York University: Are the Libraries Meeting the Needs of Faculty Members in Fine Arts?
Bibliographic record
Abstract
Objective – This study assessed the needs for digital image delivery to faculty members in Fine Arts at York University in order to ensure that future decisions regarding the provision of digital images offered through commercial vendors and licensed by the Libraries meet the needs of teaching faculty.
 
 Methods – The study was comprised of four parts. A Web survey was distributed to 62 full-time faculty members in the Faculty of Fine Arts in February of 2011. A total of 25 responses were received. Follow-up interviews were conducted with nine faculty members. Usage statistics were examined for licensed library image databases. A request was posted on the electronic mail lists of the Art Libraries Society of North America (ARLIS-L) and the Art Libraries Society of North America Canada Chapter (CARLIS-L) in April 2011 requesting feedback regarding the use of licensed image databases. There were 25 responses received.
 
 Results – Licensed image databases receive low use and pose pedagogical and technological challenges for the majority of the faculty members in Fine Arts that we surveyed. Relevant content is the overriding priority, followed by expediency and convenience, which take precedence over copyright and cleared permissions, resulting in a heavy reliance on Google Images Search.
 
 Conclusions – The needs of faculty members in Fine Arts who use digital images in their teaching at York University are not being met. The greatest shortcomings of licensed image databases provided by the Libraries are the content and technical challenges, which impede the ability of faculty to fully exploit them. Issues that need to be resolved include the lack of contemporary and Canadian content, training and support, and organizational responsibility for the provision of digital images and support for the use of digital images.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.113 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".