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This paper seeks to examine image retrieval within two different contexts: a monolingual context where the language of the query is the same as the indexing language and a multilingual context where the language of the query is different from the indexing language. The study also aims to compare two different approaches for the indexing of ordinary images representing common objects: traditional image indexing with the use of a controlled vocabulary and free image indexing using uncontrolled vocabulary.

This research uses three data collection methods. An analysis of the indexing terms was employed in order to examine the multiplicity of term types assigned to images. A simulation of the retrieval process involving a set of 30 images was performed with 60 participants. The quantification of the retrieval performance of each indexing approach was based on the usability measures, that is, effectiveness, efficiency and satisfaction of the user. Finally, a questionnaire was used to gather information on searcher satisfaction during and after the retrieval process.

The results of this research are twofold. The analysis of indexing terms associated with all the 3,950 images provides a comprehensive description of the characteristics of the four non‐combined indexing forms used for the study. Also, the retrieval simulation results offers information about the relative performance of the six indexing forms (combined and non‐combined) in terms of their effectiveness, efficiency (temporal and human) and the image searcher's satisfaction.

The findings of the study suggest that, in the near future, the information systems could benefit from allowing an increased coexistence of controlled vocabularies and uncontrolled vocabularies, resulting from collaborative image tagging, for example, and giving the users the possibility to dynamically participate in the image‐indexing process, in a more user‐centred way.

Studies in corporate social responsibility (CSR) disclosure were initially focused more on disclosure “Quantity” than “Quality” and while they have started to explore “Disclosure Quality”, their assessment mechanisms are found to be immature. Thus, while a number of papers have sought to assess the quality of CSR disclosure, this paper aims to suggest an approach tied closely to both expectations in assessing “quality” derived from the Conceptual Framework for Financial Reporting (revised 2018) and the global reporting initiative. The outcome is to offer a best practice approach to assessing CSR disclosure quality.

In this paper, prior literature is reviewed, qualitative characteristics from the Conceptual Framework for Financial Reporting (revised 2018) and globally recognised guidelines such as the GRI are reviewed. The framework for a “CSR disclosure quality index” as an assessment tool to assess CSR disclosure quality is developed from qualitative characteristics and criteria identified.

The proposed CSR disclosure quality index is developed in stages from the qualitative characteristics identified in the Conceptual Framework for Financial Reporting (revised 2018) and criteria identified from the guidelines discussed. A table was then developed linking the qualitative characteristics to criteria providing a Likert scale approach to assessing the disclosures made by companies to make an assessment of the quality of the companies’ reports. It is argued this provides a robust assessment, being a direct and comprehensive measure of disclosure quality.

As with most qualitative work, there are alternative approaches to establishing an index, but the authors believe this is an approach offering links (and, therefore, credibility) to globally recognised guidelines in the assessment of CSR disclosure quality. Future work could enhance the alignment of this index with the sustainable development goals (SDGs), building on the preliminary connections established in this study.

At a practical level this index offers an approach to reviewing the quality of CSR disclosures which could prove useful to policymakers and in the future development and expansion of this framework offering greater objectivity to assessments and justification for proposed improvement in reporting practice. Also, this index serves as a benchmarking tool for companies to meet the disclosure expectations of stakeholders.

This approach has the potential to substantially fulfil stakeholder expectations by addressing the growing demand for transparency in this area, while avoiding practices that could be perceived as superficial or misleading (greenwashing). Focusing on social issues enables stronger connections between companies and their stakeholders. Furthermore, the index helps companies link their CSR efforts with SDGs and show their commitment to long-term social value building in discussion of governance factors to show accountability expectations are being met.

This paper contributes to CSR disclosure quality literature and provides a reliable method of assessing the quality of CSR disclosures. Opportunities for further and broader developments can be envisaged while offering a credible and reliable approach.

The purpose of this paper is two‐fold: to examine three models of subject indexing (i.e. expert‐led indexing, author‐generated indexing, and user‐orientated indexing); and to compare and contrast two user‐orientated indexing approaches (i.e. the theoretically‐based Democratic Indexing project, and Flickr, a working system for describing photographs).

The approach to examining Flickr and Democratic Indexing is evaluative. The limitations of Flickr are described and examples are provided. The Democratic Indexing approach, which the authors believe offers a method of marshalling a “free” user‐indexed archive to provide useful retrieval functions, is described.

The examination of both Flickr and the Democratic Indexing approach suggests that, despite Shirky's claim of philosophical paradigm shifting for social tagging, there is a residing doubt amongst information professionals that self‐organising systems can work without there being some element of control and some form of “representative authority”.

This paper contributes to the literature of user‐based indexing and social tagging.

The overwhelming speed and scale of digital media production greatly outpace conventional indexing methods by humans. The management of Big Data for e-library speech resources requires an automated metadata solution. The paper aims to discuss these issues.

A conceptual model called semantic ontologies for multimedia indexing (SOMI) allows for assembly of the speech objects, encapsulation of semantic associations between phonic units and the definition of indexing techniques designed to invoke and maximize the semantic ontologies for indexing. A literature review and architectural overview are followed by evaluation techniques and a conclusion.

This approach is only possible because of recent innovations in automated speech recognition. The introduction of semantic keyword spotting allows for indexing models that disambiguate and prioritize meaning using probability algorithms within a word confusion network. By the use of AI error-training procedures, optimization is sought for each index item.

Validation and implementation of this approach within the field of digital libraries still remain under development, but rapid developments in technology and research show rich conceptual promise for automated speech indexing.

The SOMI process has been preliminarily tested, showing that hybrid semantic-ontological approaches produce better accuracy than semantic automation alone.

Even as testing proceeds on recorded conference talks at the University of Tebessa (Algeria), other digital archives can look toward similar indexing. This will mean greater access to sound file metadata.

Huge masses of spoken data, unmanageable for a human indexer, can prospectively find semantically sorted and prioritized indexing – not transcription, but generated metadata – automatically, quickly and accurately.

This paper aims to determine and compare the vulnerability of selected indigenous communities in the Philippines using several integrated index development approaches. Better understanding on how negative impacts of climate change could be effectively reduced is the identification of appropriate vulnerability assessment approach that is applicable to the local and cultural contexts. Critical in the case of indigenous communities is the analyses of determinants of vulnerability by and with themselves.

Using the survey data of combined 169 households, vulnerability scores of two adjacent Alangan Mangyan indigenous communities were quantified utilizing a set of 31 indicators that were systematically combined via the balanced-weighted (composite index) and unbalanced-weighted (principal component analysis) approaches.

The computed vulnerability scores of the Alangan Mangyan communities using several approaches yielded varying results. In both study sites, the degree of vulnerability is differentiated even among households of indigenous communities of comparable socio-economic characteristics. The developed indices confirm that typhoons, level of education, literacy rate and monthly income were found to have direct effect on the Alangan Mangyan communities’ vulnerability.

The study has successfully tested various methodological frameworks in implementing vulnerability assessment applicable in the context of indigenous communities in the Philippines. Results highlighted the need to simultaneously implement several vulnerability assessment approaches to allow comparison of results instead of solely basing climate change vulnerability-reduction programs to be implemented to a single assessment approach.

This paper aims to offer an extensive empirical case study analysis by investigating housing affordability in Turkey as a whole, and in Istanbul, Ankara and Izmir over the period of 2006 and 2017 and its sub-periods.

This paper develops a theoretically informed model to assess affordability using complementary methodologies in quantitative analysis. This study seeks to help outline the nature of the problem in aggregate level and in the cities; it also seeks to offer lessons about how to address measurement and modelling challenges in emergent market contexts by constructing aggregate-/city-level housing cost-to-income (HCI) ratio, adjusted HCI (AHCI) ratio, housing affordability index (HAI) and effective HAI sensitive to multiple calculation methodologies and alternative data set involving income distribution and poverty tranches.

HCI, AHCI, HAI and EHAI models generally suggest the parallel results: housing is not affordable in Turkey and in Istanbul, Ankara and Izmir except for the highest income groups. The evidence implies that besides macroeconomic instabilities, distorted interest rates and short average mortgage maturity, poverty and unequal income/wealth distributions are the main reasons of the Turkish housing affordability crisis specifically heightened in metropolitan areas such as in Istanbul.

The evidence provides an insight on housing affordability problems in Turkey. However, small sample size and short observation period create a limit for generalisation of the findings. Further analysis would be required to illustrate how housing affordability changes in different cities of Turkey in a longer period.

By using empirical approaches, this paper helps to understand how serious housing affordability problems of Turkey in aggregate and urban levels. This evidence helps to explain declining ownership ratio in low-income groups and in urban areas. Reliable explanations on existing housing crisis of Turkey also help to develop affordable housing policies.

Declining housing affordability and homeownership ratio may translate as the rising housing inequality and insecurity among Turkish households. Moreover, better affordability values of higher income groups suggest that existing inequality, economic/social segmentation, and hence social tension between high and low income groups, may further increase. In this respect, the authors suggest socially important policies such as reducing income/wealth inequalities and increasing affordable housing supply.

This study offers a detailed empirical case study analysis that can be used as an exemplar of how to overcome data constraints in other evolving housing market contexts. This study sets out an approach overcoming the challenges of measurement. This study also combines existing methodological approaches with the modified variables to provide a more realistic aggregate-/urban-level housing affordability picture. The authors calculated some parts of housing affordability ratio and index series using discretionary income, minimum wage and effective minimum wage to show the variations of different measurement approaches. Some constructed series are also sensitive to income distribution and poverty thresholds. Collectively, this empirical approach, developed by using emerging market data, provides a contribution to the literature.