Bibliographic record
Abstract
What is information? This question echoes through the field of information science and the many attempts at defining the essence of the field are well documented. For graduate students entering the field, answering this question is an intimidating prospect. This paper presents an exploratory, ethnographic study as a thematic narrative exploring the way three first-year library and information science (LIS) students at the University of Toronto’s Faculty of Information conceptualize information and how the iSchool environment—in which diverse approaches to studying information beyond LIS are present— has impacted their perspective. An extension of the draw-and-write technique was used to have participants create a visual representation of their current understanding of information and how they would have conceptualized it before becoming an iSchool student. Paired with guided tours of the iSchool environment, participants conveyed that information was messy and manifests through a variety of behaviours and sources. The iSchool environment provides information and students pay attention to the values and aesthetics conveyed through physical and digital interactions with the Faculty. Ultimately, the paper argues that students go beyond recognizing how their own understanding of “information” is influenced by their time at the iSchool and come to appreciate diverse perspectives that exist within their cohort and the field while embracing the complex and at times messy nature of information.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.064 |
| Scholarly communication | 0.022 | 0.043 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".