The David and Goliath principle: Cultural, ideological, and attitudinal underpinnings of the normative protection of low-status groups from criticism

Two studies documented the “David and Goliath” rule—the tendency for people to perceive criticism of “David” groups (groups with low power and status) as less normatively permissible than criticism of “Goliath” groups (groups with high power and status). The authors confirmed the existence of the David and Goliath rule across Western and Chinese cultures (Study 1). However, the rule was endorsed more strongly in Western than in Chinese cultures, an effect mediated by cultural differences in power distance. Study 2 identified the psychological underpinnings of this rule in an Australian sample. Lower social dominance orientation (SDO) was associated with greater endorsement of the rule, an effect mediated through the differential attribution of stereotypes. Specifically, those low in SDO were more likely to attribute traits of warmth and incompetence to David versus Goliath groups, a pattern of stereotypes that was related to the protection of David groups from criticism.

Performative digital asset management: To propose a framework and proof of concept model that effectively enables researchers to document, archive and curate their non-traditional research data

This cross disciplinary study was conducted as two research and development projects. The outcome is a multimodal and dynamic chronicle, which incorporates the tracking of spatial, temporal and visual elements of performative practice-led and design-led research journeys. The distilled model provides a strong new approach to demonstrate rigour in non-traditional research outputs including provenance and an 'augmented web of facticity'.

Patient safety and the RCA: A document analysis

This research examined the function of Queensland Health's Root Cause Analysis (RCA) to improve patient safety through an investigation of patient harm events where permanent harm and preventable death, Severity Assessment Code 1, were the outcome of healthcare. Unedited and highly legislated RCAs from across Queensland Health public hospitals from 2009, 2010 and 2011 comprised the data. A document analysis revealed the RCAs opposed organisational policy and dominant theoretical directives. If we accept the prevailing assumption that patient harm is a systemic issue, then the RCA is failing to address harm events in healthcare.

Document timespan normalisation and understanding temporality for clinical records search

Previous qualitative research has highlighted that temporality plays an important role in relevance for clinical records search. In this study, an investigation is undertaken to determine the effect that the timespan of events within a patient record has on relevance in a retrieval scenario. In addition, based on the standard practise of document length normalisation, a document timespan normalisation model that specifically accounts for timespans is proposed. Initial analysis revealed that in general relevant patient records tended to cover a longer timespan of events than non-relevant patient records. However, an empirical evaluation using the TREC Medical Records track supports the opposite view that shorter documents (in terms of timespan) are better for retrieval. These findings highlight that the role of temporality in relevance is complex and how to effectively deal with temporality within a retrieval scenario remains an open question.

Semi-supervised document clustering via loci

Document clustering is one of the prominent methods for mining important information from the vast amount of data available on the web. However, document clustering generally suffers from the curse of dimensionality. Providentially in high dimensional space, data points tend to be more concentrated in some areas of clusters. We take advantage of this phenomenon by introducing a novel concept of dynamic cluster representation named as loci. Clusters’ loci are efficiently calculated using documents’ ranking scores generated from a search engine. We propose a fast loci-based semi-supervised document clustering algorithm that uses clusters’ loci instead of conventional centroids for assigning documents to clusters. Empirical analysis on real-world datasets shows that the proposed method produces cluster solutions with promising quality and is substantially faster than several benchmarked centroid-based semi-supervised document clustering methods.

Scalable document hashing and retrieval

This thesis studies document signatures, which are small representations of documents and other objects that can be stored compactly and compared for similarity. This research finds that document signatures can be effectively and efficiently used to both search and understand relationships between documents in large collections, scalable enough to search a billion documents in a fraction of a second. Deliverables arising from the research include an investigation of the representational capacity of document signatures, the publication of an open-source signature search platform and an approach for scaling signature retrieval to operate efficiently on collections containing hundreds of millions of documents.

Parents as co-researchers at home: Using an observational method to document young children’s use of technology

This paper discusses the use of observational video recordings to document young children’s use of technology in their homes. Although observational research practices have been used for decades, often with video-based techniques, the participant group in this study (i.e., very young children) and the setting (i.e., private homes), provide a rich space for exploring the benefits and limitations of qualitative observation. The data gathered in this study point to a number of key decisions and issues that researchers must face in designing observational research, particularly where non-researchers (in this case, parents) act as surrogates for the researcher at the data collection stage. The involvement of parents and children as research videographers in the home resulted in very rich and detailed data about children’s use of technology in their daily lives. However, limitations noted in the dataset (e.g., image quality) provide important guidance for researchers developing projects using similar methods in future. The paper provides recommendations for future observational designs in similar settings and/or with similar participant groups.

Mining topical relevant patterns for multi-document summarization

Multi-document summarization addressing the problem of information overload has been widely utilized in the various real-world applications. Most of existing approaches adopt term-based representation for documents which limit the performance of multi-document summarization systems. In this paper, we proposed a novel pattern-based topic model (PBTMSum) for the task of the multi-document summarization. PBTMSum combining pattern mining techniques with LDA topic modelling could generate discriminative and semantic rich representations for topics and documents so that the most representative and non-redundant sentences can be selected to form a succinct and informative summary. Extensive experiments are conducted on the data of document understanding conference (DUC) 2007. The results prove the effectiveness and efficiency of our proposed approach.