Bibliographic record
Abstract
This paper is based upon the 2020 Miles Conrad Award Lecture that was given by James G. Neal at the inaugural NISO Plus conference held from February 23–25, 2020 in Baltimore, MD (USA). The lecture provided a brief look back at some of the key information industry challenges of the past four decades, but more importantly it highlighted the challenges facing the community today such as the democratization of creativity; the born-digital explosion; policy chaos including privacy, market monopoly, global intellectual property, and intellectual freedom; the challenges of diversity, equity, and inclusion; and human-machine symbiosis and blended reality. The lecture lists five commandments to which all stakeholders in the Information Community need to adhere in order to be successful moving forward together: (1) Thou shall preserve the cultural and scientific record; (2) Thou shall fight the information policy wars; (3) Thou shall be supportive of the needs of your users and your readers; (4)Thou shall cooperate in new and more rigorous ways; and (5) Thou shall work together to improve knowledge creation, evaluation, distribution, use, and preservation.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.034 |
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".