MétaCan
Menu
Back to cohort
Record W2905878493 · doi:10.1002/9781119083054.ch12

Using Parent Knowledge to Enhance Teaching and Learning Experiences in Schools for Children and Youth

2018· other· en· W2905878493 on OpenAlexaff
Debbie Pushor

Bibliographic record

Venuenot available
Typeother
Languageen
FieldComputer Science
TopicEducational Challenges and Innovations
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSituatedCraftAffect (linguistics)PsychologyContext (archaeology)Body of knowledgeNatural (archaeology)Descriptive knowledgePedagogyDevelopmental psychologyMathematics educationSocial psychologyKnowledge managementCommunicationComputer scienceGeography

Abstract

fetched live from OpenAlex

Craft and professional knowledge is the knowledge which arises from the teaching, learning, and experiences in which one is immersed as one develops one's hobbies and pursues a career path over time. Just as parent knowledge is situated in the mind and body of the parent, it also appears to be situated in abstract and non-rational ways in the metaphysical. Parent knowledge is uncertain knowledge because it is constructed, held, and used in the dynamic context of a family, a unit of people who are always in flux. To engage parents is to establish a side by side relationship with them, in which their parent knowledge is used alongside the teacher's knowledge in decisions which affect them and their children. With authentic engagement, parents maintain their unique positioning as parents, interacting with their children in the natural ways in which parents do, and not as parents who are imitating teachers.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.066
GPT teacher head0.374
Teacher spread0.308 · 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
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

Citations5
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicEducational Challenges and InnovationsFrench-language works237,207