Sample size and informetric model goodness-of-fit outcomes: a search engine log case study
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
The influence of sample size on informetric characteristics is examined to determine whether theoretical mathematical models can adequately fit large data sets. Two large data sets of queries submitted to the Excite search service were sampled for search characteristics (term frequencies, terms used per query, pages viewed per query, queries submitted per session) producing data sets of various sizes that were fitted to theoretical models to determine how the sample may influence a model’s goodness-of-fit. Although theoretical models could adequately fit smaller data sets of up to 5000 observations in some cases, larger data sets could not be satisfactorily fitted using several goodness-of-fit techniques. Investigators must take into account that sample size does influence goodness-of-fit outcomes. The nature of the data and not the limitations of given goodness-of-fit tests results in significant outcomes. Such goodness-of-fit tests should be used for comparative purposes, rather than significance testing.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.015 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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