The Internet as a scientific tool for studying leisure activities: exploratory Internet data collection
Bibliographic record
Abstract
There are times when researchers want to examine sizeable set of leisure activities, often those pursued by a particular demographic category or those grouped according a particular theoretic classification. Yet, conventional qualitative methods are poorly suited to gathering the broad range of data they require for this purpose. These methods are too labour‐intensive, while quantitative surveys, if they are to be effective, are limited to known populations which can be properly sampled. But the need to gather data on sets of leisure activities persists; for considering all the leisure activities pursued in the world today, we have some, not even full, ethnographic knowledge of only a very small proportion. One way to solve this problem is through exploratory Internet data collection (EIDC): searching the Internet for exploratory qualitative data on large sets of leisure activities. The nature of the data found on the Internet and the sources there in which these data may be found are discussed. The Internet can be a rich source of descriptive, ethnographic, data. The advantages and disadvantages of EIDC are considered. Nine types of Internet data are set out. The issues of ethics and author copyright are also addressed. Most of the information gathered through EIDC is unavailable elsewhere, or available only in very limited fashion.
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.
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 | 0.039 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".