Exploring the validity of electronic newspaper databases
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 Do electronic newspaper databases contain all of the stories that appear in the print edition? And does this depend on the database used? To explore these questions, we collected print copies of newspapers from cities across the USA and Canada. We compared coverage of two topics in these newspapers with the coverage obtained from keyword searches in three electronic newspaper databases. We conclude that the stories obtained through electronic searches are consistent across databases but can vary from the print source. Importantly, national and international coverage is more likely to be missing than local or statewide/provincial coverage. Keywords: content analysisvalidityelectronic databasesnewspapers Notes 1. ProQuest began distributing electronic material on CD-ROM in the 1980s and moved to electronic distribution in 1996. Lexis-Nexis began providing desktop access to select newspapers using personal computers in 1979; web-based distribution began in 1994 and catered to legal professionals. It is unclear when NewsBank began distributing newspaper content electronically. 2. These dates were chosen for convenience but seemed fairly unremarkable in terms of news content. The big national story in most newspapers was Tea Party protests across the USA. The big international story was piracy off the coast of Somalia. 3. This does raise the issue of whether one's results are consistent over time. For another project, we repeated these searches on various days and found very little variation at all depending on the search day. Indeed, we got the exact same number of hits when we repeated the search on 18 May as on 11 May. 4. The Boston Herald does not have a 'metro' section per se, as it is a tabloid. We therefore searched for articles from the first and second pages of the newspapers. At the time we conducted this analysis, the Des Moines Register was no longer available in any of the three databases. 5. To be consistent with our electronic searchers, the coder did not note mentions of 'Mr. Obama,' 'President Obama,' or other variations on his name in the print copies of the newspapers. 6. Each of the electronic databases allows one to search wire service stories separately, which may be helpful in determining whether a newspaper uploads wire service content to the database. 7. Interview was conducted via email on 21 October 2009. 8. Graber relies upon data from The State of the News Media 2004 report, produced by the Project for Excellence in Journalism. 9. Quite simply, since approaches to uploading content vary from newspaper to newspaper and from database to database, we mean that the researcher would need to sit down and scan all of the results retrieved to see whether any letters to the editor, editorials, photo captions, or wire service stories (i.e. those types of content often excluded from an electronic database) appear or not. 10. Google. News (publishers) help. 'Restricted Content.' Retrieved 16 August 2011, from http://www.google.com/support/news_pub/bin/answer.py?hl=en&answer=68331.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.021 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it