Bibliographic record
Abstract
“Big Data” is a technological term with a seemingly cognitive connotation that masks an ideological orientation of those attempting to be benevolently, criminally of even “innocently” in control of our knowledge and subsequent actions. Without an epistemological foundation “small” and especially “big” data are a myth. When “the truth” becomes “what’s on a digital screen” under the control of those in charge of “the cloud” we are clouding our cultural heritage voluntarily to an extent that exposes us to the whims of those screening and displaying our data even in so-called “post-truth” fashion. Subsequent information and knowledge cannot be critically and rationally assessed for lack of evidence. All lessons learned during the last four centuries of enlightening efforts seem to be forgotten or ignored by us. Our preference for “cognitive ease” can be easily abused by those in control of modern information technology. We remain in “self-imposed immaturity” (Kant) while they can act primarily for their own economic, political, and social benefits and may even feel “justified” by the big-data-ideology. Knowledge must remain relevant to, testable and rationally believable by the legitimate recipients of any public data and information. An enlightened framework for data governance is overdue in the “digital big data age!”
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 | 0.052 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.072 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".