Review: A Short History of Denver by Stephen J. Leonard and Thomas J. Noel
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
Book Review| November 01 2018 Review: A Short History of Denver by Stephen J. Leonard and Thomas J. Noel A Short History of Denver. By Stephen J. Leonard and Thomas J. Noel. (Reno, University of Nevada Press, 2016. xviii + 184 pp.) Elaine Naylor Elaine Naylor Mount Allison University Search for other works by this author on: This Site PubMed Google Scholar Pacific Historical Review (2018) 87 (4): 726–727. https://doi.org/10.1525/phr.2018.87.4.726 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Elaine Naylor; Review: A Short History of Denver by Stephen J. Leonard and Thomas J. Noel. Pacific Historical Review 1 November 2018; 87 (4): 726–727. doi: https://doi.org/10.1525/phr.2018.87.4.726 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentPacific Historical Review Search This content is only available via PDF. © 2018 by the Pacific Coast Branch, American Historical Association2018 Article PDF first page preview Close Modal You do not currently have access to this content.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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