Bibliographic record
Abstract
Web archive derivatives of the General collection from Columbia University Libraries. The derivatives were created with the Archives Unleashed Toolkit and Archives Unleashed Cloud. The cul-1716-parquet.tar.gz derivatives are in the Apache Parquet format, which is a columnar storage format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See this notebook for examples. Domains .webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc) Produces a DataFrame with the following columns: domain count Web Pages .webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content")) Produces a DataFrame with the following columns: crawl_date url mime_type_web_server mime_type_tika content Web Graph .webgraph() Produces a DataFrame with the following columns: crawl_date src dest anchor Image Links .imageLinks() Produces a DataFrame with the following columns: src image_url Binary Analysis Audio Images PDFs Presentation program files Spreadsheets Text files Word processor files The cul-1716-auk.tar.gz derivatives are the standard set of web archive derivatives produced by the Archives Unleashed Cloud. Gephi file, which can be loaded into Gephi. It will have basic characteristics already computed and a basic layout. Raw Network file, which can also be loaded into Gephi. You will have to use that network program to lay it out yourself. Full text file. In it, each website within the web archive collection will have its full text presented on one line, along with information around when it was crawled, the name of the domain, and the full URL of the content. Domains count file. A text file containing the frequency count of domains captured within your web archive.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.027 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.601 | 0.642 |
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".