Libraries, data and the fourth industrial revolution (Data Deluge Column)
Bibliographic record
Abstract
Purpose The World Economic Forum held in Davos, Switzerland, in January 2016, brought together leaders from the areas of science and technology, business, health, education, government and other fields as well as representatives from the media. A key theme of the forum was what has come to be known as the “fourth industrial revolution”. Design/methodology/approach News reports and blog posts about the forum gave the impression that this new “revolution” would bring unprecedented advances in science and medicine as well as would hold the potential for a future dominated by intelligent robots and massive levels of unemployment. Findings For example, on January 24, 2016, Elliot of The Guardian reported that the “Fourth Industrial Revolution brings promise and peril for humanity”. Sensational headlines and sound bites are good at attracting attention but they are not very effective with regard to communicating what this revolution is about and what it could mean for our lives, communities, governments and our workplaces in the near and distant future. The snippets of information reported here and there give the impression that robots, artificial intelligence, cloud-based computing, big data and a combination of other technologies are gradually merging to create a new reality which has the potential for revolutionizing our way of life. Originality/value This installment of the Data Deluge consists of an exploration of the fourth industrial revolution, what role libraries might play in this revolution and how our information environment could be forever changed.
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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.028 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.109 | 0.056 |
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".