Bibliographic record
Abstract
Most discussions of search engines focus on technology or user experience. By contrast, this paper asks about those who produce the recommendations that search engines gather. How are these people and institutions affected when search engines incorporate their work into search results, but no credit is given? The paper argues that the lack of attribution encourages the myth of automated meaning, the false belief that computers and algorithms have created rather than simply gathered these recommendations. It further argues that by concealing the role of these producers, search engines undermine public support for the individuals and institutions that create trustworthy recommendations, especially libraries. Because search engines borrow so extensively from public institutions and the public at large, their ethical obligations are far greater than previously recognized. The paper concludes with some comparisons between the ethical practices of libraries and those of search engines. Acknowledgements: An earlier version of this paper was delivered at the Symposium Ethics of Electronic Information in the 21st Century, 2005. I would like to thank Mardi Mahaffy for commenting on the paper.
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 | 0.046 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.137 |
| Scholarly communication | 0.015 | 0.035 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".