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Record W2972956434 · doi:10.20381/ruor-23833

The Stories We Tell Ourselves: Collective Memory and the Ontario Grade 10 History Curriculum

2019· dissertation· en· W2972956434 on OpenAlexaboutno aff
Merina Shulist

Bibliographic record

VenueuO Research (University of Ottawa) · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumCollective memoryMathematics educationHistoryPedagogyPsychologyPolitical science

Abstract

fetched live from OpenAlex

Collective memory, though fluid and evolving, plays a key role in strengthening a community’s bonds and identity through a common past and values and shared commemorations. The tools and methods of reinforcing and transmitting a community’s collective memory are varied and some are more obvious than others. One under-examined avenue of transmission and commemoration is the formal education system, and official curriculums in particular, despite their explicit role of teaching in a group setting. To better grasp this relationship, the portrayals of four military conflicts, WWII, the Korean War, and the October and Oka Crises, and the course’s skills and goals were compared within three editions of the Ontario Grade 10 History curriculum and with public memory at the time each was issued. The connections revealed between curricular content and the wider public memory as well as changes to the community’s overall collective memory support the role of the history curriculum as a means of transmission and commemoration and as a record of the community’s collective memory at a specific point in time.

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)0.006
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 score0.136
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.009
Scholarly communication0.0070.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.127
GPT teacher head0.371
Teacher spread0.244 · 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
Published2019
Admission routes1
Has abstractyes

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