Bibliographic record
Abstract
Purpose The purpose of this paper is to illustrate how associationism mistakenly assumes that direct experience is possible; that is, there is expectation-free observation and association without prior expectation. Thus, associationism assumes that learning involves the absorption of information from the environment itself. However, contrary arguments take the position that, for an individual to make a connection between his/her behaviour and its consequence(s), he/she must first have an expectation in order for a connection to be made. Design/methodology/approach This paper uses a personal experience to illustrate how implicit assumptions and unstated expectations can be found in the corporate world. It offers to answer questions that will lead to an examination of overcoming irrationality through utilization of the Popperian philosophy of associationism. Findings When evaluating a practice, it is easier to find evidence of some sort to support the practice, especially if we are either disposed to do so or if our colleagues and organizations have recommended that we adopt these practices. However, if we are committed to genuinely improving our practice, Swann (2009) suggests that we become critical and ask, “What are the unintended and undesirable consequences of doing things this way?” (p. 8). Research limitations/implications Popper’s approach needs to be developed or learned through stages and with time. We need to be aware that it takes time to master the use of this approach. Merely introducing or having organizations learn the different methods or short cuts have only a limited effect in improving their ability to deal with issues in different contexts. Practical implications The examples used throughout this paper illustrate that the adoption of Popper’s approach does not necessarily require large-scale experiments. In fact, a well-conducted case study can be effective in casting doubt on existing assumptions. Regardless of the nature of the research strategy and the scale of the experiment devised to test a hypothesis, the task of testing can and will be problematic. Social implications Expectations can make us look foolish from time to time, but they can also be very powerful or useful because they are more than mere anticipation. If we are unable to strip away our preconceptions or prior knowledge, we can at least acknowledge our biases and, in doing so, we may not continue to be trapped within our own perspectives, which can blind us to the truth. Originality/value The examples used in this paper illustrate that Popper’s approach is robust and applicable in a variety of contexts and is not limited to educational organizations. Furthermore, it showcases our irrationality, and helps us understand when and where we may make erroneous decisions.
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.
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 | 0.053 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.010 | 0.109 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".