Bibliographic record
Abstract
As illustrated in the preceding chapters, social and ethical concerns about technology are multifaceted and cannot be resolved through methods derived from any one discipline. Instead, a multi-tiered approach that draws on an interdisciplinary knowledge base is recommended to guide a proper technoethical inquiry advanced through knowledge and insights derived from multiple disciplines and literatures. This approach is desirable for achieving a more comprehensive picture of technology at the core of human life and society. Knowledge derived from the cross-fertilization of relevant areas of inquiry represents a potentially powerful set of knowledge building tools that can be used for maximizing the positive and minimizing the negative ethical aspects of technology in society. To this end, a systems approach to technoethical inquiry (chapter 4) was highlighted as an ideal methodology for studying the multi-faceted nature of ethical aspects of technology. This, however, does not negate the use of other methods and tools available to guide technoethical inquiry. Neither does it capture the nature and scope of technoethical inquiry within the real world of technology and humans.
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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".