Bibliographic record
Abstract
The "On-Demand Book Service (ODBS)" is a collaboration between First Nations communities in Northern Ontario and academic researchers from the University of Toronto. The aim of the ODBS is to bridge the gap between physical and digital libraries. The latest workshop (organized in March of 2010) dealt with issues of reading in First Nations communities; and included the shipping of three complete sets of ODBS equipment to three Northern Ontario communities. In addition, graduate students were sent to various sites (Thunder Bay, Sioux Lookout and Keewaywin) to meet in person with community members, act as facilitators, and assist with setting up the equipment and getting a sense of potential uses. Indeed, there is an obvious service gap that technology and equipment cannot bridge, and the on-going challenge remains the articulation of a community-driven strategy. This poster will present initial feedback gathered by following up with the event participants, the facilitators, as well as the community members in the various sites. Their diverse perspectives present a holistic picture of the On-Demand Book Service as perceived by the different stakeholders, and may hold great insights to other such information and library science projects that attempt to bridge great geographical and cultural distances.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 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".