Exploring approaches for classifying ornamental garden plant purchasers
Bibliographic record
Abstract
Abstract Involvement scales have been widely used to measure the extent to which a product is associated with an individual's self‐concept, and the hedonic pleasure evoked by the activity or product. A number of studies have linked involvement with higher overall spending on a product. This study aimed to determine whether gardening involvement predicted increased garden plant purchasing behavior in Canada and to understand the implications of high gardening involvement by comparison with other measures, both subjective (self‐assessed expertise) and objective (hours spent gardening, objective gardening knowledge). Gardening involvement did not predict purchasing behavior nor did self‐assessed gardening expertise. However, objective measures (hours spent gardening and objective gardening knowledge) were found to predict plant purchasing. It is suggested that the involvement scale be used in combination with objective measures to distinguish between consumers with high product interest but low present use and those with high interest and high product use. Practical applications Although involvement was not found to predict garden plant‐purchasing behavior, by measuring involvement it is possible to identify individuals who have a high level of interest in a product or activity. In doing so, involvement helps to identify all potential users, some of whom may not be captured with questions around current product purchasing behavior. These users can be subdivided with objective measures according to those that are presently high product users and those that are low product users. Low product users with high involvement may require an additional incentive to engage with the product/activity due to barriers such as lack of knowledge or time. By combining the involvement scale with product usage information, it is possible to identify two sub‐groups of high product involvement individuals who can be targeted with customized advertising or versions of a product in order to attract a broader audience.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".