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Record W3159769701 · doi:10.46991/sbmp/2019.2.2(4).020

CAN YOU T.E.A.C.H. ADOLESCENTS?

2019· article· en· W3159769701 on OpenAlexaff

Bibliographic record

VenueModern Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychologyRealmEmpathyHarmony (color)Vulnerability (computing)PedagogySocial psychologyMathematics educationComputer science

Abstract

fetched live from OpenAlex

The main question underlying this study refers to how teachers may be helped to address the psychological factors associated with the well-being of their adolescent students. In the realm of professional development, the teachers are offered to review their background knowledge about the psychology of adolescents and use what research and experience have taught to support, encourage and enhance their students’ learning. The adolescent is obviously central to the equation with his/her typical characteristics of increased detachment from adults, overall emotional vulnerability, the emphasis on the peer group and the essential search for identity. The second refrain has to do with the various roles of the teacher as a person, what he/she needs to know and how he/she needs to be in order to provide his/her students with the opportunities to learn in ways that meet their various needs. Finally, the third and last chapter elaborates on what T.E.A.C.H. means: T for thought; E for empathy; A for activity; C for choice and H for harmony. It also provides a discussion about how each aspect evolves with time and integrates within the scope of research relative to the domain of teaching and psychology.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0190.005

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 head0.020
GPT teacher head0.343
Teacher spread0.323 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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