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 purpose of this paper is to assess information technology (IT) disaster recovery plans (DRPs) in publicly listed companies on Abu Dhabi securities exchange (ADX) in the United Arab Emirates. The authors assessed, among other things, DRP preparedness, documentation, employees’ preparedness and awareness and the most significant physical and logical risks that pose the most threads to drive the development of the DRP, etc. Design/methodology/approach The authors surveyed publicly listed companies on the ADX using a questionnaire adapted from past research papers as well as from audit programs published by the Information Systems Audit and Control Association. The surveys were completed through interviews with middle and senior management familiar with their firm’s IT practices. Findings The majority of the respondents reported having a DRP, and a significant number of the respondents reported that their top management were extremely committed to their DRP. Employees were generally aware of their role and the existence of the DRP. The greatest risk/threat to their organization’s IT system was logical risk followed closely by power and network connectivity loss as the second highest physical risk. The most highly ranked consequence of an IT disaster was loss of confidence in the organization. Research limitations/implications Because this paper only examined publicly listed companies on ADX, the research results may lack generality. Therefore, further research is needed in this area for determining the extent of the deployment of the DRP in the region. Practical implications Results of this paper could be used for IT DRP planning bench-marking purposes. Originality/value This paper adds value to research by investigating the current IT DRP practices by public companies listed on ADX.
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 | 0.004 |
| Open science | 0.000 | 0.000 |
| 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