MétaCan
Menu
Back to cohort
Record W2115332657 · doi:10.22230/ijepl.2014v9n5a562

Failure, The Next Generation: Why Rigorous Standards are not Sufficient to Improve Science Learning

2014· article· en· W2115332657 on OpenAlexvenueno aff
Mary Antony Bair, David E. Bair

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumSpeculationState (computer science)Science policyScience educationMathematics educationPolicy analysisPolitical scienceProcess (computing)SociologyPublic relationsPublic administrationPedagogyPsychologyBusinessComputer scienceFinance

Abstract

fetched live from OpenAlex

Although many states in the United States are adopting policies that require all students to complete college-preparatory science classes to graduate from high school, the outcomes of such policies have not always led to improved student outcomes. While there is much speculation about the cause of the dismal results, there is scant research on the process by which the policies are being implemented at the school level, especially in schools that enroll large numbers of historically non-college-bound students. To address this gap in the literature, we conducted a four-year ethnographic case study of policy implementation at one racially and socioeconomically diverse high school in Michigan. Guided by the structuration theory of Anthony Giddens, we gathered and analyzed information from administrator and science teacher interviews, observations of science classes, and relevant curriculum and policy documents. Our findings reveal the processes and rationales by which a state policy mandating three years of college-preparatory science for all students was implemented at the school. Four years after the policy was implemented there was little improvement in science outcomes. The main reason for this, we found, was the lack of correspondence between the state policy and local policies developed in response to that state policy.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.821
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.269
GPT teacher head0.434
Teacher spread0.165 · 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 teacher head, 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

Citations9
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Education Policy and LeadershipSame topicEducator Training and Historical PedagogyFrench-language works237,207