Revolutionary Reading, Evolutionary Toolmaking: (Re)development of Scholarly Reading and Annotation Tools in Response to an Ever Changing Scholarly Climate
Bibliographic record
Abstract
As the online scholarly landscape changes, so too must the tools used to traverse it. The Public Knowledge Project (PKP) Reading Tools provides readers a bridge from online scholarly content to a host of contextual information, to a number of discipline-specific search engines and databases, and to other tools. A lot has changed since it was originally released, such as the rise of Google Scholar as the de facto starting point for many novice (and not-so novice) researchers; the blurring line between desktop and web applications; and the increased professional use of social networking tools and websites. Recently, the University of Victoria's Electronic Textual Cultures Lab (ETCL), in cooperation with the PKP, undertook a study to determine the role and value of the existing Reading Tools, particularly in the context of Humanities Computing. The ETCL has also developed a prototype Professional Reading Environment which has been the basis for substantial analysis. Rick Kopak and Chia-Ning Chiang at the University of British Columbia (UBC) have undertaken a broad survey of the online annotation landscape, and have written a proposal for developing an annotation system for PKP software. This paper discusses how, using this research as a base and in cooperation with UBC and the PKP, the ETCL has begun a large-scale redevelopment of the PKP Reading Tools, extending the current toolset to include new social networking and research tools, as well as a robust personal annotation system, making social annotation possible between small groups and the public.
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 | 0.071 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.030 | 0.055 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".