The Hidden Curriculum in Schools: Implications for Lesbian, Gay, Bisexual, Transgender, and Queer Youth
Bibliographic record
Abstract
Since the inception of public schools, curriculum has been a source of ongoing debate and disagreement.Educational bureaucrats, school board administrators, principals, vice principals, teachers, parents, community organizations, and even students weigh in on discussions about what stu- dents should learn in class, how learning is best facilitated, and how to measure and evaluate such learning.Textbooks, teaching practice, projects, homework, examinations, report cards, and the like, are the purview of the formal curriculum.However, students learn in ways that lie beyond the boundaries of the formal curriculum through what is known as the hidden curriculum (Jackson, 1968).The hidden curriculum as a theoretical construct is conceptually problematic.As does the "formal" curriculum, the hidden curriculum defies definitional consensus.Nevertheless, the hidden curriculum will refer here to student learning that takes place within the perimeter of a school that is not recorded or reflected within the official curriculum.Theorists are not unanimous about the effects of the hidden curricu- lum on students and on teaching practice.The phrase "the hidden curric- ulum" explicates its defining characteristic, namely, that it is hidden.Unlike the official curriculum, the hidden curriculum does not make reference to tangible, constituent materials and outcomes measurable though standardized procedures or established protocols.By comparison, the hidden curriculum is a concept that seems ambiguous.Some 1 .Gerald Walton is a PhD Candidate at Queen's University and will de- fend his dissertation in 2006.18
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".