Bibliographic record
Abstract
As the perceived risk associated with cannabis use is decreasing, the rise of the number of people trying cannabis and becoming regular users is increasing. Furthermore, as Canadian law makers are currently in the process of legalizing cannabis, it is crucial to gain a greater understanding of both the immediate and long-term effects associated with cannabis use. There is clear evidence that the intoxicating effects of cannabis include impairment of cognitive abilities. The duration of the acquired deficits is unclear, however, and the evidence implicating harmful effects to brain structure is ambiguous. Several methodological challenges will need to be addressed to empirically address these important gaps in our knowledge about cannabis. The present review considers reports from investigations of cannabis effects on verbal learning and memory, premorbid intellect, and spatial working memory as measured by the Rey Auditory Verbal Learning Test (RAVLT), WAIS-IV/WASI-III Vocabulary Subtest, and WAIS-IV Spatial Span Task, respectively. Variability in prior results may relate to methodological differences that include sample age or exposure to cannabis (dose, frequency, duration). A lack of consensus on the most appropriate cognitive measures also limits cross-study comparison. As such, it is imperative for research to use the most effective methods to study the potential effects cannabis has on cognition. With the upcoming legalization of cannabis it is urgent to better understand the effects of cannabis use; whether beneficial, harmful, or non-existent. Discipline: Psychology (Honours) Faculty Mentor: Dr. Alexander Penney
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".