Bibliographic record
Abstract
This examines the three main themes throughout the book: us versus them, technology as tool, and library as place. Us versus them highlights the relationships that librarians have with their various user communities and even with other librarians. Librarians use technology to position themselves as technology experts, which places users in a subordinate position. Amongst themselves, librarians use technology to distinguish between those who are concerned with patrons' needs and open-minded about the best way to address them and those who are closed-minded and anti-technology. Additionally, librarians use technology to distinguish themselves from LIS faculty members by claiming that faculty members are too distanced from the actual uses of technology in the profession. Technology as tool is perhaps the most dominant theme throughout the book. By understanding technology as just a tool, librarians end up defining themselves by how they use technology, thus limiting not only their use of it, but also placing inadvertent limits on how it can be used within the library itself to provide services. Lastly, technology has changed how librarians understand the library as place. The library, in the face of technological change, has become a place that needs protecting. Librarians, as a result, have become the protectors of the library as place. They use technology in a controlled way to manage this.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.112 | 0.057 |
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".