Bibliographic record
Abstract
Web archive derivatives of the Resistance collection from Columbia University Libraries. The derivatives were created with the Archives Unleashed Toolkit and Archives Unleashed Cloud. The <strong>cul-8752-parquet.tar.gz</strong> 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. <strong>Domains</strong> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> Produces a DataFrame with the following columns: domain count <strong>Web Pages</strong> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> Produces a DataFrame with the following columns: crawl_date url mime_type_web_server mime_type_tika content <strong>Web Graph</strong> <pre><code class="language-java">.webgraph()</code></pre> Produces a DataFrame with the following columns: crawl_date src dest anchor <strong>Image Links</strong> <pre><code class="language-java">.imageLinks()</code></pre> Produces a DataFrame with the following columns: src image_url <strong>Binary Analysis</strong> PDFs Spreadsheets Text files Word processor files<br> The <strong>cul-8752-auk.tar.gz </strong>derivatives<strong> </strong>are the standard set of web archive derivatives produced by the Archives Unleashed Cloud. <strong>Gephi </strong>file, which can be loaded into Gephi. It will have basic characteristics already computed and a basic layout. <strong>Raw Network</strong> file, which can also be loaded into Gephi. You will have to use that network program to lay it out yourself. <strong>Full text</strong> 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. <strong>Domains count</strong> 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.013 |
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; both teacher heads agree on what is shown here.
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".