Tag Archives: information

Reasons for publishing scientific papers

A few months ago I wrote about how we are drowning in information as a result of the two million papers published in journals every year [see ‘We are drowning in information while starving for wisdom‘ on January 20th, 2021]. As someone who has published about 10 papers each year for the last couple of decades, including three this year already, I feel I should provide some explanation for continuing to contribute to the deluge of papers. I think there are four main reasons for publishing scientific papers. First, to report a discovery – a new contribution to knowledge or understanding.  This is the primary requirement for publication in a scientific journal but the significance of the contribution is frequently diminished both by the publisher’s and author’s need to publish which leads to many papers in which it is hard to identify the original contribution. The second reason is to fulfil the expectations or requirements of a funding agency (including your employer); I think this was probably the prime driver for my first paper which reported the results of a survey of muskoxen in Greenland conducted during an expedition in 1982. The third reason is to support a promotion case, either your own or one of your co-authors; of course, this is not incompatible with the reporting original contributions to knowledge but it can be a driver towards small contributions, especially when promotion committees consider only the quantity and not the quality of published papers. The fourth reason is to support the careers of members of the research team; in some universities it is impossible to graduate with a PhD degree in science and engineering without publishing a couple of papers, although most supervisors encourage PhD students to publish their work in at least one paper before submitting their PhD thesis, even when it is not compulsory. Post-doctoral researchers have a less urgent need to publish unless they are planning an academic career in which case they will need a more impressive publication record than their competitors. Profit is the prime reason for most publishers to publish papers.  Publishers make more money when they sell more journals with more papers in them which drives the launch of new journals and the filling of journals with more papers; this process is poorly moderated by the need to ensure the papers are worth reading.  It might be an urban myth, but some studies have suggested that half of published papers are read only by their editor and authors.  Thirty years ago, my PhD supervisor, who was also my mentor during my early career as an academic, already suspected this lack of readers and used to greet the news of the publication of each of my papers as ‘more stuffing for your chair’.


Patterson, E.A., 1984, ‘Sightings of Muskoxen in Northern Scoresby Land, Greenland’, Arctic, 37(1): 61-63

Rose Eveleth, Academics write papers arguing over how many people read (and cite) their papers, Smithsonian Magazine, March 25th, 2014.

Image: Hannes Grobe, AWI, CC BY-SA 2.5 <https://creativecommons.org/licenses/by-sa/2.5&gt;, via Wikimedia Commons.

Four requirements for consciousness

Max Tegmark, in his book Life 3.0 – being a human in the age of artificial intelligence, has taken a different approach to defining consciousness compared to those that I have discussed previously in this blog which were based on synchronous firing of assemblies of neurons [see, for example, ‘Digital hive mind‘ on November 30, 2016 or ‘Illusion of self‘ on February 1st, 2017] and on consciousness being an accumulation of sensory experiences [Is there a real ‘you’ or’I’? on March 6th, 2019].  In his book, Tegmark discusses systems based on artificial intelligence; however, the four principles or requirements for consciousness that he identifies could be applied to natural systems: (i) Storage – the system needs substantial information-storage capacity; (ii) Processing – the system must have substantial information-processing capacity; (iii) Independence – the system has substantial independence from the rest of the world; and (iv) Integration – the system cannot consist of nearly independent parts.  The last two requirements are relatively easy to apply; however, the definition of ‘substantial’ in the first two requirements is open to interpretation which leads to discussion of the size of neuronal assembly required for consciousness and whether the 500 million in an octopus might be sufficient [see ‘Intelligent aliens?‘ on January 16th, 2019].


Max Tegmark,  Life 3.0 – being a human in the age of artificial intelligence, Penguin Books, Random House, UK, 2018.

Image: Ollie the Octopus at the Ocean Lab, (Ceridwen CC BY-SA 2.0)


Epistemic triage

A couple of weeks ago I wrote about epistemic dependence and the idea that we need to trust experts because we are unable to verify everything ourselves as life is too short and there are too many things to think about.  However, this approach exposes us to the risk of being misled and Julian Baggini has suggested that this risk is increasing with the growth of psychology, which has allowed more people to master methods of manipulating us, that has led to ‘a kind of arms race of deception in which truth is the main casualty.’  He suggests that when we are presented with new information then we should perform an epstemic triage by asking:

  • Is this a domain in which anyone can speak the truth?
  • What kind of expert is a trustworthy source of truth in that domain?
  • Is a particular expert to be trusted?

The deluge of information, which streams in front of our eyes when we look at the screens of our phones, computers and televisions, seems to leave most of us grasping for a hold on reality.  Perhaps we should treat it all as fiction until have performed Baggini’s triage, at least on the sources of the information streams, if not also the individual items of information.


Julian Baggini, A short history of truth: consolations for a post-truth world, London: Quercus Editions Ltd, 2017.

Deep long-term learning

About six months ago I wrote about providing feedback to students [see post entitled ‘Feedback on feedback’ on June 28th, 2017].  I wrote that students tend to find negative feedback unfair and unhelpful, even when it is carefully and politely worded; but they like clear, unambiguous, instructional and directional feedback [1].  I suspect the same could be said for their teachers, many of whom fear [2] or are anxious about [3] their next student evaluation of teaching (SET) report even though they tend to see SET data as useful [2].  Some university teachers are devastated by negative feedback [4], with inexperienced and female teachers being more sensitive and more likely to experience negative feelings [5].  What follows below is a brief review, but long blog post, on the usefulness of student evaluation of teaching with the bottom line being: student evaluations of teaching have serious limitations when the goal is to instill deep long-term learning in a culture that values teachers.

Student evaluations of teaching (SET) are widely used in higher education because collecting the data from students at the end of each term is easy and because the data is useful in: improving teaching quality; providing input to appraisal exercises; and providing evidence of institutional accountability [2].  However, the unresolved tension between the dual use of the data for teacher development and as a management tool [2, 6] has led to much debate about the appropriateness and usefulness of student evaluation of teaching with strong advocates on both sides of the argument.

For instance, there is evidence that students’ perception of a lecturer significantly predicts teaching effectiveness ratings, with the charisma of the lecturer explaining between 65% [7] to 69% [8] of the variation in ‘lecturer ability’; so that student evaluations of teaching have been described as ‘personality contests’ [9].  Some have suggested that this leads to grading leniency, i.e. lecturers marking students more leniently in order to attract a higher rating, though this argument has been largely refuted [7]; but, there are several studies [10-12] that report a negative association between a pessimistic attitude about future grades and ratings of teacher effectiveness.

However, of more concern is the evidence of student fatigue with teaching evaluations, with response rates declining during the academic year and from year 1 to 4, when adjusted for class size and grades [6].  Student completion rates for end-of-term teaching evaluations are influenced by student gender, age, specialisation, final grade, term of study, course of study and course type. This means that the respondent pools do not fully represent the distribution of students in the courses [6].  Hence, a knowledge of the characteristics of the respondents is required before modifications can be made to a course in the best interests of all students; but such knowledge is rarely available for SET data.  In addition, the data is usually not normally distributed [13] implying that common statistical practices cannot be deployed in their interpretation, with the result that the lack of statistical sophistication amongst those using SET information for appraisal and promotion leads to concerns about the validity of their conclusions [8].

However, recent research creates much more fundamental doubts about the efficacy of SET data.  When learning was measured with a test at the end of the course, the teachers who received the highest SET ratings were the ones who contributed most to learning; but when learning was measured as performance in subsequent courses, then the teachers with relatively low SET ratings appeared to have been most effective [14-16].  This is because making learning more difficult can cause a decrease in short-term performance, as well as students’ subjective rating of their own learning, but can increase long-term learning.  Some of these ‘desirable’ difficulties are listed below.  So, if the aim is to instill deep long-term learning within a culture that values its teachers then student evaluations of teaching have serious limitations.


[1] Sellbjer S, “Have you read my comment? It is not noticeable. Change!” An analysis of feedback given to students who have failed examinations. Assessment & Evaluation in HE, 43(2):163-174, 2018.

[2] Spooren P, Brockx B & Mortelmans D, On the validity of student evaluation of teaching: the state of the art, Review of Educational Research, 83(4):598-642, 2013.

[3] Flodén J, The impact of student feedback on teaching in higher education, Assessment & Evaluation in HE, 42(7):1054-1068, 2017.

[4] Arthur L, From performativity to professionalism: lecturers’ responses to student feedback, Teaching in Higher Education, 14(4):441-454, 2009.

[5] Kogan LR, Schoenfled-Tacher R & Hellyer PW, Student evaluations of teaching: perceptions of faculty based on gender, position and rank, Teaching in Higher Education, 15(6):623-636, 2010.

[6] Macfadyen LP, Dawson S, Prest S & Gasevic D, Whose feedback? A multilevel analysis of student completion of end-of-term teaching evaluations, Assessment & Evaluation in Higher Education, 41(6):821-839, 2016.

[7] Spooren P & Mortelmanns D, Teacher professionalism and student evaluation of teaching: will better teachers receive higher ratings and will better students give higher ratings? Educational Studies, 32(2):201-214, 2006.

[8] Shevlin M, Banyard P, Davies M & Griffiths M, The validity of student evaluation of teaching in Higher Education: love me, love my lectures? Assessment & Evaluation in HE, 24(4):397-405, 2000.

[9] Kulik JA, Student ratings: validity, utility and controversy, New Directions for Institutional Research, 27(5):9-25, 2001.

[10] Feldman KA, Grades and college students’ evaluations of their courses and teachers, Research in Higher Education, 18(1):2-124, 1976.

[11] Marsh HW, Students’ evaluations of university teaching: research findings, methodological issues and directions for future research, IJ Educational Research, 11(3):253-388, 1987.

[12] Millea M & Grimes PW, Grade expectations and student evaluation of teaching, College Student Journal, 36(4):582-591, 2002.

[13] Gannaway D, Green T & Mertova P, So how big is big? Investigating the impact of class size on ratings in student evaluation, Assessment & Evaluation in HE, 43(2):175-184, 2018.

[14] Carrell SE & West JE, Does professor quality matter? Evidence from random assignment of students to professors. J. Political Economics, 118:409-432, 2010.

[15] Braga M, Paccagnella M & Pellizzari M, Evaluating students’ evaluation of professors, Econ. Educ. Rev., 41:71-88, 2014.

[16] Kornell N & Hausman H, Do the best teachers get the best ratings? Frontiers in Psychology, 7:570, 2016.