Gaps in Post-Birth Care in Neoliberal Times: Evidence from Canada
Bibliographic record
Abstract
Research in a number of countries shows an increasing trend for maternal services to focus on infant health, and on the surveillance of parents and their parenting skills, rather than on the provision of broad ranging support for mothers (Dennis et al., 2007; Zadoroznyj, 2006). A wide range of reports produced in the United Kingdom (UK) over the past several years, for example, have centred on the notion of ‘early years’ or ‘foundation years’ interventions, with the aim to reduce child poverty and inequality in life chances, and to ultimately forestall persistent social problems linked to early parental neglect (Allen, 2011; Field, 2010). Within these reports, the importance of mothers’ mental and physical health is cited in relation to childhood health and well-being, but the proposed solutions are most often short-lived, rather superficial interventions such as brief visits by ‘health visitors’, often aimed at screening for risks. Additionally, such visits are framed almost exclusively in terms of expected improvements in mother-child bonding (‘attachment’) and/or breastfeeding rates, and their alleged consequences for early brain development and immunity, rather than any substantive improvements in the health and overall well-being of mothers themselves (Marmot et al., 2010). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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".