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Record W2408447908 · doi:10.1017/s1474746415000536

Introduction: Young Fatherhood: Lived experiences and policy challenges

2015· article· en· W2408447908 on OpenAlexaboutno aff
Bren Neale

Bibliographic record

VenueSocial Policy and Society · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsnot available
FundersEconomic and Social Research Council
KeywordsDisadvantagedQuarter (Canadian coin)Teenage pregnancyPoliticsFalling (accident)Psychological interventionPolitical scienceSociologyGender studiesEconomic growthPsychologyDemographyGeographyEconomicsPopulation

Abstract

fetched live from OpenAlex

The entry of young people into early parenthood has long been regarded as an issue for social policy and for professional practice in the UK and internationally. Despite a steadily falling trend, most notably since 1998, the UK still has one of the highest rates of teenage pregnancy in Europe, concentrated in the most socially disadvantaged areas of the country (Office for National Statistics, 2015). The majority of these pregnancies are unplanned, with about half resulting in the birth of a child, although the extent to which this should be a cause for concern is a contested issue (Duncan et al. , 2010). Considerable research evidence exists on the experiences of young mothers, with a range of interventions designed to meet their needs. However, young fathers (defined as those under the age of 25, a quarter of whom are estimated to be in their teens) have, until recently, been neglected in both research and policy. Over the past decade, small pockets of research evidence on the circumstances, practices and values of young fathers have begun to coalesce into a fledgling evidence base. However, the notion of ‘feckless’ young men, who are assumed to be absent, or disinterested in ‘being there’, or, worse, regarded as a potential risk to their children, continues to hold sway, particularly in popular media and some political discourses (Neale and Davies, 2015).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.368
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2015
Admission routes1
Has abstractyes

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