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Record W2903928615 · doi:10.1177/1476718x18818205

Institutional mapping as a tool for resource consultation

2018· article· en· W2903928615 on OpenAlexafffund
Kathryn Underwood, Alison Smith, Julia Martin

Bibliographic record

VenueJournal of Early Childhood Research · 2018
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsResource (disambiguation)Field (mathematics)Service (business)Concept mapKnowledge managementWork (physics)SociologyComputer scienceBusinessEngineering

Abstract

fetched live from OpenAlex

This article explores how mapping is currently being used in social services research as well as practice, and discusses the potential for mapping to be a useful tool for resource consultants in the field. This article will review different mapping techniques, including community mapping, eco-mapping, journey mapping and institutional mapping, and discuss their purpose within research and professional practice. Using examples from the Inclusive Early Childhood Service System project, we describe the application of mapping in social service work, and how resource consultants can use these techniques in their own practice.

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)0.007
metaresearch head score (Gemma)0.005
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 score0.682
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.106
GPT teacher head0.430
Teacher spread0.324 · 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

Citations18
Published2018
Admission routes2
Has abstractyes

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