Bibliographic record
Abstract
This chapter aims to improve the rigor and legitimacy of Web-traffic measurement as a social research method. I compare two dominant forms of Web-traffic measurement and discuss the implicit and largely unexamined ontological and epistemological claims of both methods. Like all research methods, Webtraffic measurement has implicit ontological and epistemological assumptions embedded within it. An ontology determines what a researcher is able to discover, irrespective of method, because it provides a frame within which phenomena can be rendered intelligible. I argue that Web-traffic measurement employs an ostensibly quantitative, positivistic ontology and epistemology in hopes of cementing the “scientific” legitimacy they engender. But these claims to “scientific” method are unsubstantiated, thereby limiting the efficacy and adoption rates of log-file analysis in general. I offer recommendations for improving these measurement tools, including more reflexivity and an explicit rejection of truth claims based on positivistic science.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".