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Record W3112050518 · doi:10.1177/0886109920978575

Data Collection Strategies for Sharing Lived Experiences: Low-Income Mothers’ Perceptions of Text (SMS) and Multimedia (MMS) Data Collection

2020· article· en· W3112050518 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAffilia · 2020
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsData collectionPerceptionQualitative propertyQualitative researchPsychologyComputer scienceMedical educationMultimediaMedicineSociology

Abstract

fetched live from OpenAlex

The purpose of this study was to explore low-income lone mothers’ perceptions of their engagement with a text (SMS) and multimedia message (MMS) qualitative study. Study participants were asked to submit text and pictures via SMS and MMS that represented their reflections, observations, and experiences accessing community support services over a 6-week period. After engaging in the study, participants were asked to complete an evaluation questionnaire. The resoundingly positive feedback received in the evaluation—and the researchers’ satisfaction with the data collected—suggests that researchers should consider employing SMS and MMS data collection strategies to gain open-ended insights into the daily experiences of marginalized groups. This method may be particularly well suited to feminist research designs and research with populations underrepresented in the literature due to barriers presented by traditional data collection strategies.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0030.002
Research integrity0.0000.000
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 head0.109
GPT teacher head0.318
Teacher spread0.209 · 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