Hidden Online Surveillance: What Librarians Should Know to Protect Their Own Privacy and That of Their Patrons
Bibliographic record
Abstract
Librarians have a professional responsibility to protect the right to access information free from surveillance. This right is at risk from a new and increasing threat: the collection and use of non-personally identifying information such as IP addresses through online behavioral tracking. This paper provides an overview of behavioral tracking, identifying the risks and benefits, describes the mechanisms used to track this information, and offers strategies that can be used to identify and limit behavioral tracking. We argue that this knowledge is critical for librarians in two interconnected ways. First, librarians should be evaluating recommended websites with respect to behavioral tracking practices to help protect patron privacy; second, they should be providing digital literacy education about behavioral tracking to empower patrons to protect their own privacy online.
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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 | 0.027 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.019 | 0.059 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".