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Unconditional Care in Context

2022· book· en· W4306944953 on OpenAlexaff
John S. Sprinson, Ken Berrick

Bibliographic record

Venuenot available
Typebook
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsSeneca Polytechnic
Fundersnot available
KeywordsContext (archaeology)Intervention (counseling)Social workEvictionDisadvantagedAgency (philosophy)Public relationsSocial isolationCriminologyPovertyEconomic JusticeHuman servicesPolitical scienceSociologyPsychologyMedicineNursingLawSocial science

Abstract

fetched live from OpenAlex

Abstract Unconditional Care in Context examines the multiple, interacting social adversities that confront system-involved families and children and argues that intervention with these young people regularly fails to acknowledge the effects of these challenges. Assessment and treatment practices often focus only on relational and behavioral forces at work within individual children and their families. The book reviews the ways in which intervention in the child welfare, public behavioral health, education, and juvenile justice systems omits the daily realities of lives that are constrained and disrupted by the injuries of racism, the limitations imposed by poverty, the threat of eviction and homelessness, the danger of community violence and crime, and the compounding effects of social disconnection and isolation. These challenges never operate in isolation and are never brief or fleeting. Instead, for many system-involved families they are chronic and cumulative and amplify each other. They also can infiltrate internal life and undermine the developing child’s sense of personal agency and value. Unconditional Care in Context reviews the tangled effects of multiple social adversities and offers a roadmap to reclaiming these problems as appropriate, vital concerns of human service agencies and their workers. It offers concrete recommendations for “ecologically informed practice” at the level of the family, at the level of schools and communities, and at the level of state and national policy. Current examples of program innovations and policy initiatives that move in this direction are reviewed.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.005
Scholarly communication0.0040.004
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0340.005

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 head0.049
GPT teacher head0.413
Teacher spread0.364 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreOther

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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