The Over-Claiming Technique: Measuring Self-Enhancement Independent of Ability

Overclaiming is a concrete operationalization of self-enhancement based on respondents’ ratings of their knowledge of various persons, events, products, and so on. Because 20% of the items are nonexistent, responses can be analyzed with signal detection formulas to index both response bias (over-claiming) and accuracy (knowledge). Study 1 demonstrated convergence of over-claiming with alternative measures of self-enhancement but independence from cognitive ability. In Studies 2–3, the validity of the overclaiming index held even when respondents were (a) warned about the foils or (b) asked to fake good. Study 3 also showed the utility of the over-claiming index for diagnosing faking. In Study 4, the over-claiming technique was applied to the debate over the adaptive value of positive illusions

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Self-reports of intelligence: are they useful as proxy measures of IQ?

Correlations between single self-ratings of intelligence and IQ are rather small (.20-.25) in college samples. Possible improvements in traditional methods were investigated by employing (a) indirect questions and (b) aggregation. To evaluate these improvements, we compared the validity of aggregated and unaggregated versions of direct measures with four indirect measures: Gough's Intellectual efficiency scale, Hogan's Intellect composite scale, Sternberg's Behavior Check List, and Trapnell's Smart scale. We also compared the performance of a novel self-report measure, Paulhus' Over-Claiming Questionnaire, which shares properties of IQ tests and self-report measures. All measures were administered to two large samples of undergraduates (Ns = 310, 326), who also took an IQ test. Results with traditional self - reports showed that both direct and indirect measures can reliably predict IQ scores but the validity cap appears to be .30 in our competitive college sample. As a rule, the most valid of the traditional items were global characterizations of mental ability; Aggregation benefited indirect more than direct measures. The novel measure, the Over-Claiming Questionnaire, outperformed all other measures with a validity cap of about .50.Arts, Faculty ofPsychology, Department ofGraduat

Clinical and genetic characterization of pituitary gigantism: an international collaborative study in 208 patients.

Despite being a classical growth disorder, pituitary gigantism has not been studied previously in a standardized way. We performed a retrospective, multicenter, international study to characterize a large series of pituitary gigantism patients. We included 208 patients (163 males; 78.4%) with growth hormone excess and a current/previous abnormal growth velocity for age or final height >2 s.d. above country normal means. The median onset of rapid growth was 13 years and occurred significantly earlier in females than in males; pituitary adenomas were diagnosed earlier in females than males (15.8 vs 21.5 years respectively). Adenomas were ≥10 mm (i.e., macroadenomas) in 84%, of which extrasellar extension occurred in 77% and invasion in 54%. GH/IGF1 control was achieved in 39% during long-term follow-up. Final height was greater in younger onset patients, with larger tumors and higher GH levels. Later disease control was associated with a greater difference from mid-parental height (r=0.23, P=0.02). AIP mutations occurred in 29%; microduplication at Xq26.3 - X-linked acrogigantism (X-LAG) - occurred in two familial isolated pituitary adenoma kindreds and in ten sporadic patients. Tumor size was not different in X-LAG, AIP mutated and genetically negative patient groups. AIP-mutated and X-LAG patients were significantly younger at onset and diagnosis, but disease control was worse in genetically negative cases. Pituitary gigantism patients are characterized by male predominance and large tumors that are difficult to control. Treatment delay increases final height and symptom burden. AIP mutations and X-LAG explain many cases, but no genetic etiology is seen in >50% of cases

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press