English and Spanish speakers’ interpretations of L2 Chinese applicative double object constructions

This article reports an empirical study investigating whether English and Spanish speakers can reconstruct thematic structures in their second language (L2) grammars of Chinese Double Object Constructions. Data collected from an acceptability judgement task and an animation matching task suggest that learners are able to reconstruct L2 grammars to accommodate new target properties. However, it is also found that learners have difficulty removing thematic relations transferred from their first language (L1), implying that adult L2 grammars might permanently deviate from grammars of native speakers. The difficulty is accounted for on the basis of Yuan's (2014) Dormant-Feature Hypothesis, which assumes Full Transfer and that if the input provides no evidence confirming or disconfirming the transferred property, the property will lose its vigour and become dormant. This dormant status leads to random behaviours in L2 judgements and interpretations. This is confirmed in this study, in which English speakers are found to transfer one interpretation of indirect objects from their L1 and Spanish speakers are found to transfer two interpretations from their L1 that are not instantiated in the target language Chinese. Due to the misleading evidence in the Chinese input that shares surface similarity with the transferred property, English speakers are hindered from restructuring their L2 grammars, and the transferred interpretation remains active. On the other hand, the absence of informative evidence in the Chinese input leaves the two transferred interpretations to a dormant status in Spanish speakers’ L2 Chinese grammars.

Powerful sequential designs using Bayesian estimation: A power analysis tutorial using brms, the tidyverse, and furrr

Producing compelling and trustworthy results relies upon performing well-powered studies with low rates of misleading evidence. Yet, resources are limited, and maximum sample sizes required to achieve acceptable power in typical fixed N designs may be disconcerting. ‘Sequential’, ‘optional stopping’, or ‘interim’ designs – in which results may be checked at interim periods and a decision made as to whether to continue data collection or not – provide one means by which researchers may be able to achieve high power and low false positive rates with less of a resource burden. Sequential analyses have received considerable attention from both frequentist and Bayesian hypothesis testing approaches, but fewer approachable resources are available for those wishing to use Bayesian estimation. In this tutorial, we cover a general process for performing power analyses of fixed and sequential designs using Bayesian estimation – simulating data, performing regressions in parallel to reduce time requirements, choosing different stopping criteria and data collection sequences, and calculating observed power and rates of misleading evidence. We conclude with a discussion of some limitations and possible extensions of the presented approach.

Losing Knowledge by Thinking about Thinking

Defeat cases are often taken to show that even the most securely based judgment can be rationally undermined by misleading evidence. Starting with some best-case scenario for perceptual knowledge, for example, it is possible to undermine the subject’s confidence in her sensory faculties until it becomes unreasonable for her to persist in her belief. Some have taken such cases to indicate that any basis for knowledge is rationally defeasible; others have argued that there can be unreasonable knowledge. I argue that defeat cases really involve not an exposure of weakness in the basis of a judgment, but a shift in that basis. For example, when threatening doubts are raised about whether conditions are favorable for perception, one shifts from a basis of unreflective perceptual judgment to a basis of conscious inference. In these cases, the basis of one’s knowledge is lost, rather than rationally undermined. This approach to defeat clears the path for a new way to defend infallibilism in epistemology, and a new understanding of what can count as the basis of any instance of knowledge.

Predictive risk modelling and the mistaken equation of socio-economic disadvantage with risk of maltreatment

In UK, the involvement of the state in private family life has significantly changed since 1989, and is about to undergo yet further transformation with the adoption of predictive risk modelling. Its implementation by at least eight local authorities and promotion by the central government operates an ontological and methodological revolution in child protection policies and practice. This article particularly discusses the conceptual and empirical limits to their use. It makes the case that the data fed to risk models, and the institutional structures through which they are run, limit the understanding of maltreatment. Based on misleading evidence, risk models are set to mistakenly equate socio-economic disadvantage with risks, thus threatening to automatise the discrimination and alienation of the poorest sections of the population.

Conclusion

The hangman/surprise-examination/prediction paradox is solved. It is not solved by denying knowledge closure (although knowledge closure is false). It is not solved by denying KK or denying that knowing p implies other iterated knowing attitudes (although these are false). It is not solved by misleading evidence causing the students to lose knowledge because students cannot lose knowledge this way. It is solved by showing that a tacit assumption (what is being said to the students/prisoner is informative) is overlooked and that inferences by contradiction are invalid if assumptions are left out. The phenomenology of the surprise-exam paradox is explored to explain why this solution has been missed. Crucial is that in many cases the students/prisoner know(s) there will be a surprise exam/execution because of an inference from what the teacher/judge meant to say, and not directly by the literal application of what he did say.

Confidence, Belief, and Knowledge; The Vagueness of “Know(s)”

Knowledge does not require confidence. An agent may know without confidence because of misleading evidence or for other reasons. An agent may not believe what she knows. Misleading evidence never causes agents to lose knowledge. The vagueness of an expression may be visible to speakers or invisible. In the case of “bald,” it is visible; it is not visible for “know.” This is because knowledge standards are invisible. Vagueness is analyzed as being epistemic in the sense that our ignorance of whether a word applies in a case places no metaphysical constraints on the facts. Agential standards for evidence are also tri-scoped and application-indeterminate. There are cases where such standards determine no answer, knows or not; and there are cases where it is indeterminate whether, or not, standards determine an answer. Because Timothy Williamson’s argument against KK presupposes that knowledge requires confidence, his argument fails.