Bibliographic record
Abstract
During May, 2017, five exceptional undergraduate students from across the University of Toronto will join the iSquare Team, as part of the Jackman Scholars-in-Residence Program. To begin, the Scholars will be introduced to the iSquare Research Program, arts-informed visual methods, and the draw-and-write technique. Next, they will gather a new visual data set--JHiSquares—from 45 other Scholars-in-Residence; and they will learn how to manage this visual data in digital and print formats. To synthesize their experience, the group will engage the iSquare team’s Artist-in-Residence (Ms. Rebecca Noone, doctoral student) to envision and then create an arts-informed deliverable (e. g. an exhibition or performance) that captures their personal insights into the project; this research output will be shared with the JHI community. Finally, the Scholars will work with the iSquare team’s Data Manager (Ms. Stephanie Power, Faculty of Information alumna) to preserve the JHiSquares in the University of Toronto’s Dataverse, and then take the initial steps to ensure long-term access to the corpus. Ms. Christie Oh, doctoral candidate at the Faculty of Information, will assist throughout as the project's Education Manager. Overall, the Scholars-in-Residence will spend a month at a fertile site of arts-informed inquiry and experience the entire knowledge production process, that is: data creation, management, analysis, dissemination, and preservation. In return, the iSquare Research Program will be infused with the energy and new ideas from a youthful, undergraduate perspective. For more: http://www.isquares.info/jhisquares-blog
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.117 | 0.078 |
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".