Bibliographic record
Abstract
The problem of information “getting into the wrong hands” has existed since the first stored data computer systems. Numerous companies and government departments have been embarrassed by data left on un-erased media such as magnetic tape and discovered by inquiring minds. The advent of data communications brought the problem to a whole new level, since information could be transmitted over long distances to places unknown. The phenomenal rise of the Internet elevated the problem of Internet Data Persistence (IDP) to a public issue, as the “private” emails of public figures such Oliver North and Bill Gates were introduced in court proceedings, and when Delta Airlines fired a flight attendant for her in-uniform blog posting. In a significant way, the digital trail that we leave behind is becoming a new form of “online identity,” every bit as real as a passport, driver’s license or pin number. New technologies, from virtual worlds, to camera phones to video sharing sites, give the question of “Where Has My Data Gone and How Do I Really Know?” some new and frightening dimensions. Future developments like “signature by DNA biometric” will make the issue even more urgent and more complex. Coping with it will require new policies, technical tools, laws, and ethical standards. It has even been suggested that a whole new profession, sometimes called the “e-scrubber,” will arise to assist in tracking down and deleting unwanted online remnants. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.014 | 0.032 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.047 | 0.039 |
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".