Bibliographic record
Abstract
As the title of this book suggests, the knowledge society is in a state of change and transition, rather than a being a fixed point in time and history. One of the greatest objectives (and challenges) in the 21st century is to complete the transition into a knowledge society within the ever expanding landscape of information, technological advancement, and ethical tensions. The western world has survived the fallout of the ‘information bomb” and the “collapse of globalization”. Society has struggled through information overload, extended work weeks, increased time and space compression, loss of personal meaning, and a variety of new inequalities created through eroding social institutions and fragmenting social relations at a global level. Throughout this struggle, technology has acted as a focal point and catalyst, spurring ethical debates and widespread concern over the direction of society and human life. Technoethics is a key component in the advancement of the evolving knowledge society because of the central importance of both technology and humans. As demonstrated in this book, technoethical inquiry provides important insights and direction to help refocus attention on key areas of technology related human activity that raise widespread ethical concern and debate which necessitate special attention and care.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".