Bibliographic record
Abstract
The purpose of this article is to describe the scope and depth of the historic corporate annual report collections in twelve academic/research libraries in North America. For many decades, a few major academic business libraries have been collecting and preserving corporate Annual Reports (ARS), the reports sent to shareholders documenting the financial status and future plans of the company. Today these historic collections provide more than a record of the companies’ finances; they provide a glimpse into the social and cultural thoughts from the past and the corporate stories of individual companies. Digitization of these reports has only just begun; so, except for fewer than 900 companies (most of which have been on the Fortune 500 list), the companies included in these historic annual report collections are not available electronically. The combined collection of the twelve libraries includes reports from nearly 38,000 different companies. Stanford, Harvard, and Western Ontario have the largest collections. Harvard and Columbia have collections with significant historic depth, while Purdue has a collection with important late–twentieth-century holdings. The overlap of the collections is much smaller than was anticipated. The two largest collections, Stanford with over 18,000 companies and Harvard with over 11,000 companies, have only 3,668 companies in common. The overlap of companies between the largest five collections is only thirty-three companies. This research identifies these collections as unique and therefore valuable to the study of specific company histories and the industrial development in North America. Librarians need to preserve these collections and work toward digitizing them.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.021 | 0.046 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 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; 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".