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Record W7038962409

La medicalización de la infancia como optimización de la fuerza laboral en España. Higienismo, población infantil y biopolítica (1800-1850)

2022· article· en· W7038962409 on OpenAlexaboutno aff

Bibliographic record

VenueDialnet (Universidad de la Rioja) · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Ecology and Taxonomy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsPhenomenonQuarter (Canadian coin)PopulationDisciplineWork (physics)Space (punctuation)
DOInot available

Abstract

fetched live from OpenAlex

The intention of this work is to analyze the recurring dynasties of certain discourses (most of them of medical and legal inspiration, although not only) that began to break into the political scene of our country since the last quarter of the 18th century. Since then, attention to the health status of the population has increased: the high number of infant mortality, the increase in the spread of contagious diseases, the high crime rate, have caused the phenomenon of what was called the "degradation of the population”. In this case, we are going to focus on the analysis of a set of these reform proposals aimed at caring for children, raised by the most liberal pole of the Spanish sociopolitical framework of the time. Applying an archaeogenealogical examination, we intend to show how attention to the "weak population" (women and minors) facilitated the transformation of the old disciplinary model into a new model of biopolitical organization that absolutely colonized the private space of families. With the "profit for anomalies" a discourse on the family economy emerged that was unprecedented until then, which quickly managed to introduce reforms in the civil and criminal codes. Until then, no measures aimed at the child population had been contemplated in these fields.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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
Published2022
Admission routes1
Has abstractyes

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