Bibliographic record
Abstract
Since the 1970s, adult citizens of the Global North have been encouraged to learn to notice when they are physiologically stressed, and to hone skills to alleviate stress in the name of improving their mental and physical health, their social relationships, and their productivity at work. Today, many technology companies offer devices and/or apps to assist users to develop such knowledge and skills. These range from apps to teach stress recognition and relaxation techniques (such as Fitbit's and Apple Watch's breathing apps), through to worn heart-rate variability or skin conductivity sensors, and saliva and blood sampling kits to send off to laboratories to measure levels of so-called ‘stress hormones’. ‘Stress’, although remaining complex and elusive as a scientific phenomenon and experience, is rapidly becoming rearticulated through biosensing devices and platforms in ways that could have serious repercussions for how we live, including, importantly, how adults and children are monitored and assessed by remote others, such as employers, parents, teachers, social and corrective services officials, and health insurance companies. The assessment of stress via biosensing blends culturally and historically specific experiences of, ideas about and practices to ameliorate physical and psychological discomfort. Knowledges and practices developed in Eastern traditions, including yoga, meditation and mindfulness, are blended with a body of Western scientific work that dates back more than 100 years. Developed most notably by Hungarian-Canadian scientist Hans Selye (1907– 82) from the 1930s to the 1970s, this scientific work elaborated the late 19th-/early 20th-century notions of ‘internal chemical environments’ and ‘homeostasis’ posited by French physiologist Claude Bernard (1818– 78) and North American Walter Cannon (1871– 1945). As detailed later, from the 1970s, Selye's theories about the role of hormones in maintaining homeostasis, and his notions of ‘good’ and ‘bad’ stress, widely infiltrated public and clinical discourse with a narrative of ‘balance’ that can be disrupted by external stimuli such as overwork, interpersonal difficulties and major life events. This narrative also has important resonance with Chinese and other Eastern understandings of physical and mental health that were widely promulgated in the US, UK and Europe in the 1980s (Franklin et al, 2000; Jackson, 2013: 258).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".