Information Governance and Assurance: Reducing Risk, Promoting Policy
Bibliographic record
Abstract
In this chapter, we will examine the external drivers which influence organisations towards practising good information governance.These are pieces of legislation, regulation and standards which are imposed from outside the organization, and which either must be complied with in order to avoid penalties, or which define benchmarks against which the practices and performance of the organization can be judged.Sometimes these, in particular the pieces of legislation, are themselves referred to as 'information governance', in that they impose rules which govern what organizations do with information.However, as we've seen in chapter 1, a more constructive way of understanding the term is to think of 'information governance' as those practices which lead to efficient, effective and ethical use of information, the avoidance of legal repercussions being a sign of legislative recognition of the legal correctness of these practices.The specific laws and regulations dealt with in this chapter will be those which apply in the UK, as space does not permit discussion of equivalent legislation in other legislatures, but it will be found that similar legislation exists in a large number of countries -in March 2013, Rwanda became the 94 th country to pass a Right to Information Act (freedominfo.org2013), the equivalence of EC countries' data protection laws to those in the UK is discussed in section 4.10 below, as is the list of 'third' countries recognized by the EC as having equivalent legislation.Other states, including the twenty-one members of the Asia-Pacific Economic Co-operation Group (APEC) have agreed on privacy principles, and Argentina, Canada, Hong Kong, Israel and Russia have modeled their laws on the European model (Kuner 2010).The United States has had a Freedom of Information Act since 1966.It applies to records held by federal agencies, such as the Department of Justice and the Department of Health and Human Services, and gives individuals the right to access any agency record, except for those protected from public disclosure for reasons of national security, for example.It also requires the agencies to automatically publish other information, including lists of Frequently Asked Questions and answers to them (FAQs).It is the enquirer's responsibility to determine which agency has the records they require, but all agencies have a web site which lists the types of records they hold.This stance of actively making records available is endorsed as good policy by the UK Information Commissioner's Office, and we shall discuss later why it is a part of a well-thought-out information governance policy.
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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 | 0.056 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.026 | 0.037 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".