Bibliographic record
Abstract
Last week as I was basking in the leisure of the waning days of my sabbatical leave (in actual fact I was typing one of the articles that appears in this issue), a student came into my office with a question about a research design that he was analyzing.I won't go into the details of the question since it is irrelevant to the thrust of this argument.But the research question he wa~asking and his selection of variables brought to mind what I believe is one of the major conceptual errors that has plagued.and continues to plague, research in educational communication and technology.I will argue that the methodological contortions necessary to test the student's hypothesis are so cumbersome that the question should not be asked in the first place.Yet old lines of questioning persist. in spite of pleas from a variety of critics (Salomon & Clark, 1979;Clark. 1985;Salomon & Gardner, 1986).The student's research problem involved comparing mean differences of achievement among three independent variables (Le., that class of variables that are considered to be under the control of the researcher).One of the variables was gender of the student (you guessed it.the levels were male and female), a second was type of content (language content versus mathematics content) and the third was method of delivery ("computer-based instruction" versus "traditional classroom instruction") I.The sample was comprised of male and female adolescents.To begin with, it is questionable whether such a design could serve to exhibit the instructional potential of different delivery methods in interaction with student gender and content type.It is true that previous research has identified differential gender-related rates of skill development in language and mathematics.But it is the cause of these differences that is troublesome.If one subscribes to a biological/psychological explanation of sex differences in the two content areas (most people would not argue along such deterministic lines).a design of this type, or any instructionally-oriented design for that matter.has little hope of 1"Traditional teaching," as used here.refers to all forms of classroom-oriented, teacherdirected instruction.
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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 | 0.149 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.010 | 0.120 |
| Scholarly communication | 0.046 | 0.104 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".