Knowing what was done: uses of a spreadsheet log file
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
Spreadsheet use in educational environments has become widespread, likely because of the flexibility and ease of use of these tools. However, they have serious shortcomings if the teacher is to understand exactly what students or others have done. It is far too easy for students to replace a formula that gives an apparently unacceptable answer with a number that they believe to be correct. The same concern applies to recorded marks, as well as to business spreadsheets and to other reports that are used for decision-making. While intentionally misleading changes to spreadsheet files receive much attention, simple mistakes are probably more common. Some of these, such as the Trans-Alta Utilities (Globe and Mail, 2003) cut and paste error that cost the firm $24 million (US), have extreme consequences. Few are merely embarrassing. A log file or audit trail, enhanced by suitable filters, can allow both intentional and accidental changes that cause erroneous results to be caught. In order to meet these requirements, we have developed server based software tool (âTellTableâ) which allows editing, version control, and auditing of spreadsheet files. Users connect to the server using a standard web browser, and are able to access and edit spreadsheet files in a Java applet in the browser window. TellTable has been used for a pilot study to maintain marks and course information for a multi-section courses with several instructions and teaching assistants. This paper describes the TellTable software and preliminary results of the pilot test.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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