Investigation of hybrid working in data centres and its impact to the security of IT infrastructure. Case study: Data centre in Canada
Bibliographic record
Abstract
[EN] According to Kniffen et al. (2020, cited in Wang et al., 2021) the unprecedented outbreak of the Covid-19 pandemic in 2020 has required millions of people across the world into being remote workers, inadvertently leading to a de facto global experiment of remote working. However, on-site tasks are still required for some businesses such as data centres. Therefore, this research will explore the effectiveness of hybrid work in data centres and introduce a design to improve the safety and security of IT Infrastructure. Aim: To determine the effectiveness of hybrid work and improve the security of IT infrastructure in data centres. This research will investigate how hybrid working is administered in data centres and evaluate its impact to the security of IT infrastructure by comparing its implementation in data centres from different countries and a case study analysis from a data centre in Canada. Objectives: 1. List and research potentially relevant issues that may be worth investigating further. 2. Select all the issues that I would like to address according to many different aspects. 3. Collect the data for all the selected issues that I have chosen to address. 4. Analyse all this data and draw final conclusions from it. Research method: Qualitative (Methods: Online Questionnaires, Case Study) Expected outcomes: 1. Differentiate the hybrid working practices from different data centres and compare it with the data centre in Canada 2. Identify issues and propose solutions about the safety and security of IT Infrastructure (using Tableau for creating charts and Rich Pictures for illustrations) 3. Sustainable data centres 4. Forecast of hybrid working Resources required: 1. Google Scholar, Scopus, Qualtrics software, Tableau, Rich Picturing, Microsoft: Word, PowerPoint, and Excel 2. Laboratory Research Internship for the period of 120 days.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".