Bibliographic record
Abstract
Special Issue Editorial: 7th In Sickness & In Health International Research Conference: Technologies, Bodies & Health Care F rom 7-9 June 2018 we gathered on the Rozelle Campus of the University of Tasmania, inSydney, Australia to extend the tradition of the In Sickness and In Health Conferences. These conferences were born from like-minded individuals (Helsinki-7) who were interested in creating an international network of critical health scholars and scholarship in relation to power, practice and ethics in health care. At the 7th In Sickness and In Health: Technologies, Bodies and Health Care we came from around the world to engage in critical discussions regarding technology and its interface with the social and material body in health and illness. Nowadays, technologies have permeated and contributed to an ideology of effi ciency across the social, critical conversations are needed more than ever. With opportunities for critical discussions becoming increasingly rare and vitally important, I am very pleased to see us continue the conversation with an even wider audience through this special edition of Aporia – The Nursing Journal. Thank you to all authors who have contributed to this special edition of Aporia – The Nursing Journal. I also extend my gratitude to the Editor-in-Chief of Aporia – The Nursing Journal, Professor Dave Holmes, for the opportunity to continue engaging in critical conversations about the assemblages and relations between technologies, bodies and health. I hope that each and every one of you will fi nd the content of these papers thought provoking and inspiring. I look forward to continuing our conversations at the 8th conference 10-12 June, 2020: People, Origin and the End of the Universe, Lleida, Spain. https://isihconference.com/isih-2020/ Rochelle Einboden, RN, PhD Conference Chair In Sickness & In Health Conference
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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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.202 | 0.152 |
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".