MétaCan
Menu
Back to cohort
Record W7058011926

Mapping occupational engagement during long-term unemployment: Interconnections and cross-national comparisons of people, places and performances

2017· article· en· W7058011926 on OpenAlexaboutno aff

Bibliographic record

VenueCommonKnowledge Research Repository (Pacific University Oregon) · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentSet (abstract data type)Presentation (obstetrics)Process (computing)Service (business)
DOInot available

Abstract

fetched live from OpenAlex

Statement of Purpose: This presentation will report one set of findings from a two-sited, multi-year study of long-term unemployment. Rates of long-term unemployment remain higher than pre-recession estimates despite North American economies’ return to nearly full employment. To understand possibilities and boundaries for occupational engagement within the situation of long-term unemployment, we generated data at three levels in the United States and Canada: we interviewed 15 organizational stakeholders and reviewed organizational documents; we interviewed and observed 18 front-line employment support service providers; and we interviewed, observed, and completed time diaries and/or occupational maps with 23 people who self-identified as being long-term unemployed. In this presentation, we report findings from the occupational mapping process used with 18 participants.\nMethods: Occupational mapping is an elicitation method that is as much about process as it is about product. In our study, we asked participants to hand draw a map to explain the places they regularly traveled within their communities. We prompted participants to describe what was being drawn, the places depicted, activities engaged in within particular places, and modes of travel used. Once the map was completed, we asked participants to reflect on if and how their experience of long-term unemployment had implications for where they went, how they got to places, and the types of activities they needed and wanted to do. We audio-recorded all conversations during the mapping process. Our ongoing analyses of maps and accompanying transcriptions address the types of places and occupations represented; the ways in which maps and transcriptions illuminate social, political, and economic influences on occupation in each study context; common threads between maps; and omissions in maps.\nResults: We will present emerging findings from our occupational mapping process in relation to national context, gender, financial and transportation resources, and family situation. We will also integrate these findings with understandings gained through other analytic approaches used in the study, such as situational analysis and critical narrative inquiry.\nImplications: Occupational mapping can elicit details about everyday doing that are difficult to articulate using narrative methods given the tacit and experiential nature of daily occupations. It can be a useful strategy for understanding interconnections between people, places, and performances of everyday occupations in line with calls to transcend individual perspectives in occupational science. Our findings suggest that this method is a valuable means of illuminating the transactional person-environment relationships that shape occupational engagement during contemporary long-term unemployment.\nDiscussion questions: In what ways can occupational mapping augment other data generation and analysis approaches? How does occupational mapping fit within larger efforts to transcend individual perspectives in occupational science? Within a multi-level, cross-national study of long-term unemployment, what kinds of understandings does occupational mapping yield? \nKey words: Occupational mapping, long-term unemployment, critical qualitative research

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)0.005
metaresearch head score (Gemma)0.012
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 score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0040.002
Scholarly communication0.0040.003
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.072
GPT teacher head0.348
Teacher spread0.276 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCommonKnowledge Research Repository (Pacific University Oregon)Same topicMagnetic confinement fusion researchFrench-language works237,207