MétaCan
Menu
Back to cohort
Record W4378977841 · doi:10.56105/cjsae.v34i02.5650

Program Planner Dignity and Negotiation in Collaborative Projects

2023· article· en· W4378977841 on OpenAlexaffvenue
Cheryl K. Baldwin, Doug Magnuson

Bibliographic record

VenueCanadian Journal for the Study of Adult Education · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDignityNegotiationPlannerAccountabilityHierarchyPublic relationsSociologyBusinessPolitical scienceComputer scienceLawSocial science

Abstract

fetched live from OpenAlex

In this qualitative interpretivist study, we investigated the types of interactions and negotiations that supported or constrained adult education program planners’ capacity to act, conceptualized as dignity. Data were drawn from interviews with 14 program planners working in collaborative partnerships in U.S. underperforming urban schools. Planner dignity is supported by practice-focused relationships, jointly developing new practices, and program success. Dignity is constrained by organizational hierarchy, unmanageable daily expectations, and ineffective feedback mechanisms causing distance between planners and fracturing the planning table. Dignity affirmation or constraint affect planner uncertainty regarding access to students and resources, control over one’s time, and accountability. Social conditions also affect the quality of interactions. Individualistic and competitive orientations constrain dignity and impede negotiation practices. Co-operative goal orientations support bargaining and consultative problem-solving negotiations; however, these were less common. Findings advance understanding of interactions that underlie and evolve effective negotiation.

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)0.028
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.048
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0140.028
Scholarly communication0.0090.011
Open science0.0020.013
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.425
Teacher spread0.378 · 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 designNot applicable
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

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal for the Study of Adult EducationSame topicGeriatric Care and Nursing HomesFrench-language works237,207