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What do <i>They</i> Mean by “Success”?

2005· book-chapter· en· W4417078123 on OpenAlexaboutno aff
Mahboubeh Asgari, D. Kevin O’Neill

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsnot available
Fundersnot available
KeywordsHelpfulnessVariety (cybernetics)EnthusiasmReading (process)Tracking (education)Space (punctuation)Perception

Abstract

fetched live from OpenAlex

Abstract Online mentoring, or “telementoring,” is increasingly used in schools as a way to link students’ schoolwork with that of adult communities of practice and open their work to a wider responsive audience. Maintaining participants’ interest and engagement is vital in telementoring relationships, but little empirical research has directly addressed the question of what rewards participants (particularly the mentees) expect as a result. What do the participants themselves think of as success? This study developed a methodological approach to assessing mentees’ expectations and how they change. We used a variety of quantitative methods to examine possible influences on 72 adolescents’ perceptions of success in a curriculum-based telementoring program, Tracking Canada’s Past. Findings indicate that among the variables measured for this study, students’ judgments of success were best predicted by the mentors’ helpfulness in asking questions about their ongoing research, recommending reading materials, and working with the students to develop research questions or topics, along with the students’ enthusiasm for the online discussion space in which they worked with their mentors and their trust of their mentors. Further analysis examined how students’ initial desires for particular mentoring functions differed from their mentors’ initial desires to provide them, and the changes that took place in students’ expectations of specific mentoring functions between the beginning and end of the program.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.298
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0430.011

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 head0.024
GPT teacher head0.293
Teacher spread0.269 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2005
Admission routes1
Has abstractyes

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