Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Although analysis of covariance (ANCOVA) was introduced long ago, it is not well understood by many researchers and is frequently misused. ANCOVA is an extension of analysis of variance (ANOVA) with the inclusion of one or more covariates. Its benefits compared to ANOVA include (1) increased power and (2) a reduction in biases caused by differences in experimental units [the covariate(s)] among groups. ANCOVA can be presented using an adjusted means procedure (testing for differences in means adjusted for the covariate), but the procedure can be easily understood using a multiple linear regression method using indicator variables to represent groups. Statistical software packages use general linear models to perform ANCOVA which are identical to the multiple linear regression models. Failure to meet assumptions of ANCOVA can lead to misinterpretation of results. Failing to meet the assumption of parallel group regression slopes is common in many data sets and methods are available to analyze these data sets (e.g., the Johnson–Neyman technique). Although ANCOVA is robust to violations of some assumptions (e.g., normality and equality of variances) when sample sizes are equal, many nonparametric tests based on ranks are available as nonparametric alternatives to ANCOVA. WIREs Comp Stat 2011 3 260–268 DOI: 10.1002/wics.165 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it