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Record W6980563498

Children and childhood: Viewed through different disciplinary lenses

2017· article· en· W6980563498 on OpenAlexaboutno aff

Bibliographic record

VenueAnglia Ruskin Research Online (Anglia Ruskin University) · 2017
Typearticle
Languageen
FieldMedicine
TopicPectus Deformity Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsContinuanceQuarter (Canadian coin)Convention on the Rights of the ChildWelfareIdeal (ethics)DisciplineConventionWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

Children are our future, vital for the continuance of human society. They represent a sizeable proportion of the global population, so the well-being of contemporary society is dependent upon meeting their needs and developing their potential. By mid-2015, those under fifteen years old accounted for more than a quarter (26.1%) of a world total of 7.3 billion people (UN DESA, 2015). So children – their commonalities and differences, their welfare and education – must be central to global, regional and national policy. Children have always been important but have become more visible as a group in recent times, as nations developed the capacity to compute large numbers and to plan for long-term and global outcomes. This chapter focuses on these more recent times; it still reflects on change over time and place, but moves away from the historical treatment of childhood discussed in Chapter 1 to take a multi-disciplinary approach. It is interesting that UN statistics refer to children under fifteen, as the standard definition of childhood established by the 1989 UN Convention of the Rights of the Child sets the upper limit of childhood at eighteen. However, it is long recognized that, in many parts of the world, children have to assume adult responsibilities at a much younger age; the need to work to eat, a lack of educational opportunities and early marriage all play a role in positioning the age of majority below the UN ideal (Morrow, 2011).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.077
GPT teacher head0.372
Teacher spread0.295 · 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 designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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