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Record W4253596683 · doi:10.22215/etd/2016-11236

The Marginalization of Child Care: A Pilot Atlas of Child Care in Ottawa

2016· dissertation· en· W4253596683 on OpenAlexaffabout
Tara McWhinney

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicFocus Groups and Qualitative Methods
Canadian institutionsCarleton University
Fundersnot available
KeywordsChild careFocus groupCitizen journalismPublic relationsPerspective (graphical)Government (linguistics)Political sciencePsychologyNursingMedicineBusinessComputer scienceMarketing

Abstract

fetched live from OpenAlex

Coming from an 'ethic of care' perspective, this study utilizes a critical feminist approach to examine the use of participatory mapping technologies as potential tools for social policy analysis.The purpose of this research is to address whether advances in online mapping technologies can assist governments and communities with generating more experiential evidencebased policy-making.Focusing on government child care programs and policies in the Ottawa area, a cybercartographic atlas was created using information from focus groups with parents and a review of current child care programs and policies.This Pilot Child Care Atlas was successfully able to incorporate all of the child care programs and policies researched while also allowing for the imputing of the experiences of parents from the focus groups.However, in order to fully assess the usefulness of this technology for social policy analysis further research is necessary where the atlas is made available to the community. Table of AppendicesAppendix A: Recruitment Letter………………………………………………………...……..

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)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0150.008
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.021
GPT teacher head0.371
Teacher spread0.350 · 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
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
Published2016
Admission routes2
Has abstractyes

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