Bibliographic record
Abstract
After assessing and weighing all the evidence, you need to determine what would be the best course of action in answering your question and implementing a solution in practice. We call this point in the process Agree because, more often than not, you will not be the only one making the decision. Quite often library-based decisions are made in groups, which could consist of an internal group of librarians assigned to come up with a solution. Even if you do work on something by yourself, at the point of decision, others may be involved. At this point in the process you must determine the best way forward, based on your assessment of the various sources of evidence (Chapter 5). It is important to remember that we all bring biases to our interpretation of the evidence as a whole, so recognizing this is an important step toward making better decisions. In this chapter we discuss some of these factors that may influence the decision-making process. As you are working through this process, some of the questions you need to ask include whether you have looked at all the evidence openly and without prejudice; what is the best decision based on everything we know from the problem, the context and the evidence; and whether you have considered all reasonable alternatives. Ultimately, you will need to determine a course of action and begin implementation of the decision. Ideally, you would be able to reach consensus among those making the decision, based on the strength of the evidence found and considered. However, it is unlikely that this will always be the case. The more clearly you can present to others the reasons for your decision, the evidence that was reviewed and how it led you to the decision made, the easier your implementation will be at an organizational level. In this chapter we will explore some of the factors within individual and group decision making that librarians should be aware of in order to ensure that the process and implementation are as smooth as possible. Figure 6.1 illustrates the influences on effective decision making that we will discuss in this chapter.
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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 | 0.016 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.189 | 0.130 |
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".