Enhancing Key Digital Literacy Skills: Information Privacy, Information Security, and Copyright/ Intellectual Property
Bibliographic record
Abstract
Key Messages\nBackground Knowledge and skills in the areas of information security, information privacy, and copyright/intellectual property rights and protection are of key importance for organizational and individual success in an evolving society and labour market in which information is a core resource. Organizations require skilled and knowledgeable professionals who understand risks and responsibilities related to the management of information privacy, information security, and copyright/intellectual property. Professionals with this expertise can assist organizations to ensure that they and their employees meet requirements for the privacy and security of information in their care and control, and in order to ensure that neither the organization nor its employees contravene copyright provisions in their use of information. Failure to meet any of these responsibilities can expose the organization to reputational harm, legal action and/or financial loss. \nContext Inadequate or inappropriate information management practices of individual employees are at the root of organizational vulnerabilities with respect to information privacy, information security, and information ownership issues. Users demonstrate inadequate skills and knowledge coupled with inappropriate practices in these areas, and similar gaps at the organizational level are also widely documented. National and international regulatory frameworks governing information privacy, information security, and copyright/intellectual property are complex and in constant flux, placing additional burden on organizations to keep abreast of relevant regulatory and legal responsibilities. Governance and risk management related to information privacy, security, and ownership are critical to many job categories, including the emerging areas of information and knowledge management. There is an increasing need for skilled and knowledgeable individuals to fill organizational roles related to information management, with particular growth in these areas within the past 10 years. Our analysis of current job postings in Ontario supports the demand for skills and knowledge in these areas. \nKey Competencies We have developed a set of key competencies across a range of areas that responds to these needs by providing a blueprint for the training of information managers prepared for leadership and strategic positions. These competencies are identified in the full report. Competency areas include: conceptual foundations risk assessment tools and techniques for threat responses communications contract negotiation and compliance evaluation and assessment human resources management organizational knowledge management planning; policy awareness and compliance policy development project management
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".