All 2 entries tagged Learning

Reflecting Back on Researcher Development: Spring Term

A few thoughts from last week’s researcher development session on publishing, editorial work and reviewing.

Last week I co-facilitated the second workshop sessions for the Leadership and Management Development course for early-stage researchers[1]. While the course is intended to take a look and share thoughts around various aspects of researcher development, my contribution was focused on publishing – specifically editorial and reviewing work. After the previous session in November I’d reworked my contributions, as I felt after that session how there was less interest in talking and quite a bit more desire for some learning and explanatory content. As matters turned out for this second version, this was a slight error on my part, as the delegates last week were far more interested in discussions. For early-stage researchers too, they also seemed to have a much broader range of experience within publishing, which meant I could have gone much deeper into some areas of argumentation than I did!

In terms of what was covered in the session by myself, this included:

• Exchanges mission, purpose & opportunities
• Metrics, esteem and publishing
• Editorial workflows & processes
• Peer-reviewing models & ethics
• Trash publishers
• Call for papers for a forthcoming special issue

On post-event reflection, I can see my next set of materials for the summer session are going to need revision once more – possibly finding a middle way between directed learning and discursive exploration. I confess, the online nature of the workshop rather reduced the degree of interaction I felt would have benefited my session, and certainly my ability to adapt on the fly to delegates’ specific interests. It’s one reason why last terms Exchanges AMA worked so well, as I was able to let attendees specific interests direct the entire event’s focus. Certainly, even after three long years of teaching online, while I note it offers some advantages, I feel for myself at least that it forms more of an effective barrier to learning than I would like.[2]. Undoubtedly, talking to a blank screen with slides on it utterly denudes the experience for me in gaining any affective resonance with the delegates, which I rather think is to the detriment of the experience for all.

It's not that it was a terrible session – far from it[3] – I just came away thinking there was a whole lot more I could have explored, or emphasised more, than I did. This is in rather stark contrast to last month’s CADRE session where I couldn’t have been happier with the delivery and delegate response. Of course, that session was face-to-face rather than online – so this might be a personal delivery style preference. Or it might have been that, for myself at least, online sessions work best when they are discursive rather than didactic in structure. A learning point I think for my own future delivery planning.

All this aside, there were however, some wonderful questions from the delegates – and if anything the discourse part of the session was a rich exchange of insight. I learned a few things myself too in the meanwhile. So, I don’t believe my time was squandered, but I am beating myself up slightly over offering a session which I didn’t feel like it reached my normal level of teaching excellence. I can, in the final evaluation, utilise the experience to improve the next session I deliver!

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Endnotes

[1] The course lead’s preferred term for newly minted academics. Roughly analogous to early career researchers.

[2] Which is slightly concerning as, at time of writing, I’m hosting another lengthy workshop session this afternoon.

[3] Delegates may disagree!

Gareth Johnson : 28 Feb 2023 09:46 |  Tags: Development Learning Practice Reflections Researcher Teaching |  Comments (0) |  Close comments |  Report a problem

Call for Papers (themed): A.I. – Panic or Panacea

Writing about web page https://exchanges.warwick.ac.uk/index.php/exchanges/announcement/view/28

Download the full text of this call

The issue of intelligence lies at the heart of the scholarly lifeworld, although for much of history a topic focussed around a singular, human construct. Today though, algorithms, deep learning and artificial intelligence have emerged into the everyday world. From the seemingly trivial, to battling the pandemic or even fighting our future wars, applications of algorithmic intelligence are increasingly shaping critical decisions and policy helping meet emerging challenges. Should we be celebrating the transition to a more ‘automated’ workplace, freeing humankind from waged-labour exploitative drudgery or does it represent an existential threat to the livelihood of millions?

Some would argue humanity has cause to fear the unchecked rise of the machines in our society. For example, the recent examination debacle in the UK undoubtedly lays still sharp in the minds of many British students and their parents as an example of a misapplied technological aid. Other cautionary tales of unfettered algorithm use abound in fields as diverse as space imaging and earth observation, through to the evaluation of immigration applicants or ‘future crime’ prediction. Is the age of the 'Minority Report' a new era of safety to be trumpeted or a greater force for oppression and fear?

Conversely, many assert artificial intelligence, machine learning and algorithms offer humanity a brave new world of opportunity, advancement and potential achievement. Deployed in the service of humanity algorithmic intelligence could help us better plan for future building and habitation needs, predict cataclysmic acts of nature or even more efficiently discover curative treatments. Thus, the artificially intelligent enabled future may be a far brighter one than some currently anticipate. Where, if anywhere, does ‘the truth’ lay?

Manuscript Submissions

Hence, for the issue of Exchanges due for publication in Autumn 2021, we invite authors to submit original, exciting and insightful manuscripts for peer-reviewed publicationconsideration inspired by any aspect of this theme. We welcome papers written for a general academic audience exploring or reviewing the science, application and implementation of machine learning, artificial intelligence or algorithms within a broader societal setting. We also welcome submissions from the humanities, arts and social sciences dealing with the ethics, perceptions, interpretations and representations of these issues too.

First-time or early career authors may alternatively wish to consider submitting either a critical reflectionor conversational (interview) pieceinspired or informed by these themes. Such pieces would serve to provide much needed background to the topic for a general academic audience. Critical reflections and conversations only undergo editorial review ahead of publication and hence are especially suitable for first-time or early career authors.

> Our author and style guidelines are available.

Deadlines

All submitted manuscripts will undergo editorial review, with those seeking publication as a research article additionally undergoing formal peer-review. The online form should be used to make manuscript submissions.

> Peer-reviewed articles: 1st May 2021. | Conversations or critical reflections: 31st August 2021.

More information

For more information on Exchangesand our activities, visit the journal’s website. For questions relating to this call, future submissions or other matters relating to the title please contact Editor-in-Chief, Dr Gareth J Johnson.

Gareth Johnson : 03 Nov 2020 16:50 |  Tags: AI Algorithms Call Ethics Learning Machine Papers |  Comments (0) |  Close comments |  Report a problem