Sensationalising Sleep: Perspectives and Protocols for Understanding Discourses of Sleep Health in Aotearoa New Zealand
Bibliographic record
Abstract
Sufficient sleep is a basic right, vital for functioning and wellbeing. Socioecological disparities in sleep status are increasingly highlighted. However, broader social and cultural factors, including beliefs and practicalities of sleep, are seldom considered. This is particularly important for bicultural countries such as Aotearoa New Zealand, where mainstream discourses and health promotion have been colonised. Media provides a platform for shaping beliefs and attitudes concerning sleep. Media messaging contributes to definitions of 'normal sleep' and sensationalised messages around sleep(lessness) and disease-seldom accounting for nuanced differences across the lifespan or Indigenous knowledge and practises concerning sleep and wellbeing. How messages concerning sleep are delivered, interpreted, and resisted varies and warrants exploring-particularly among populations predisposed to sleep disturbances. This paper provides a narrative review of the social and cultural factors influencing sleep and highlights the paucity of research in this space. Responding to these gaps, a current research agenda is presented concerning sleep-related discourses and practises in Aotearoa New Zealand. This includes explorations of media representations of sleep, key audience interpretations, and the development of a theoretical framework to inform appropriate sleep-related research and health promotion relevant to contemporary Aotearoa New Zealand and beyond.
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 | 0.033 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".