Whither the retention schedule in the era of big data and open data?
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
Purpose – This article, which is one of the products of an international collaborative research initiative called iTrust, aims to explore these questions and offer suggestions concerning how the issues they raise can be addressed. Design/methodology/approach – The article describes the results of the first stage in a multi-stage research project leading to methods for developing retention and disposition specifications and formal schedules for open data and big data initiatives. A fictitious organization is used to describe the characteristics of open data and big data initiatives, the gap between current approaches to setting retention and disposition specifications and schedules and what is required and how that gap can be closed. The landscape described as a result of this stage in the research will be tested in case studies established in the second stage of the project. Findings – The argument is made that the business processes supporting open data and big data initiatives could serve as the basis for developing enhanced standards and procedures that are relevant to the characteristics of these two kinds of initiatives. The point is also made, however, that addressing the retention and disposition issues requires knowledge and leadership, both of which are in short supply in many organizations. The characteristics, the issues and the approaches will be tested through case studies and consultations with those involved with managing and administering big data and open data initiatives. Originality/value – There is very little, if any, current literature that addresses the impact of big data and open data on the development and application of retention schedules. The outcome of the research will benefit those who are seeking to establish processes leading to formally approved retention and disposition specifications, as well as an instrument – the approved retention and disposal schedule – designed to ensure the ongoing integrity of the records and data associated with big data and open data initiatives.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.005 | 0.005 |
| 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